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This document discusses various techniques for optimizing search space in phrase-based machine translation models, including: 1) Using graph structures and semirings like the tropical semiring to represent translation hypotheses as paths through a weighted graph and find optimal paths. 2) Applying constraints like distortion limits and beam search to prune unpromising partial translations. 3) Using heuristic functions to guide the search and pre-ordering methods like rules and learned models to reorder languages with different word orders.

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深層強化学習入門 2020年度Deep Learning基礎講座「強化学習」

深層強化学習入門．2020年6月実施の「Deep Learning基礎講座」強化学習の松嶋担当分の講義資料を再編集したものです．本資料は，資料を作成した松嶋が公開するものであり，他の講義回について，研究室としての公開は予定されていないとのことです．

Control as Inference (強化学習とベイズ統計)

The document discusses control as inference in Markov decision processes (MDPs) and partially observable MDPs (POMDPs). It introduces optimality variables that represent whether a state-action pair is optimal or not. It formulates the optimal action-value function Q* and optimal value function V* in terms of these optimality variables and the reward and transition distributions. Q* is defined as the log probability of a state-action pair being optimal, and V* is defined as the log probability of a state being optimal. Bellman equations are derived relating Q* and V* to the reward and next state value.

Mathematical preliminaries in Automata

This document provides mathematical preliminaries for automata, including:
- Sets, functions, relations, graphs, and proof techniques like induction and proof by contradiction.
- It defines sets, set operations, functions, relations, graphs, trees, and binary trees.
- It also covers topics like equivalence relations, equivalence classes, Cartesian products, and power sets.

Improved Trainings of Wasserstein GANs (WGAN-GP)

This document summarizes improved training methods for Wasserstein GANs (WGANs). It begins with an overview of GANs and their limitations, such as gradient vanishing. It then introduces WGANs, which use the Wasserstein distance instead of Jensen-Shannon divergence to provide more meaningful gradients during training. However, weight clipping used in WGANs limits the function space and can cause optimization difficulties. The document proposes using gradient penalty instead of weight clipping to enforce a Lipschitz constraint. It also suggests sampling from an estimated optimal coupling rather than independently sampling real and generated samples to better match theory. Experimental results show the gradient penalty approach improves stability and performance of WGANs on image generation tasks.

Formal systems introduction

This document contains lecture slides on formal systems and computation from a course on IST Studies taught by George Pasparakis. It introduces key concepts like formal languages, symbols and alphabets, different types of automata including finite automata, pushdown automata, and Turing machines. It also discusses the CPU components involved in computation like input memory, output memory, program memory, and temporary memory. Mathematical preliminaries covered include sets, functions, relations, graphs, and proof techniques like induction and proof by contradiction.

Random Forest

The document provides an overview of random forests, including the random forest recipe, why random forests work, and ramifications of random forests. The random forest recipe involves drawing bootstrap samples to grow trees, randomly selecting features at each split, and aggregating predictions. Random forests work by decorrelating trees, which reduces variance and leads to lower prediction error compared to individual trees. Ramifications discussed include using out-of-bag samples to estimate generalization error and calculating variable importance.

Policy Gradient Theorem

The document summarizes the policy gradient theorem, which provides a way to perform policy improvement in reinforcement learning using gradient ascent on the expected returns with respect to the policy parameters. It begins by motivating policy gradients as a way to do policy improvement when the action space is large or continuous. It then defines the necessary notation, expected returns objective function, and discounted state visitation measure. The main part of the document proves the policy gradient theorem, which expresses the policy gradient as an expectation over the discounted state visitation measure and action-value function. It notes that in practice the action-value function must be estimated, and proves the compatible function approximation theorem, which ensures the policy gradient is computed correctly when using an estimated action-value

Seminar - Similarity Joins in SQL (performance and semantic joins)

seminar regarding similarity joins both on performance size and on advanced methods for semantic joins

深層強化学習入門 2020年度Deep Learning基礎講座「強化学習」

深層強化学習入門．2020年6月実施の「Deep Learning基礎講座」強化学習の松嶋担当分の講義資料を再編集したものです．本資料は，資料を作成した松嶋が公開するものであり，他の講義回について，研究室としての公開は予定されていないとのことです．

Control as Inference (強化学習とベイズ統計)

The document discusses control as inference in Markov decision processes (MDPs) and partially observable MDPs (POMDPs). It introduces optimality variables that represent whether a state-action pair is optimal or not. It formulates the optimal action-value function Q* and optimal value function V* in terms of these optimality variables and the reward and transition distributions. Q* is defined as the log probability of a state-action pair being optimal, and V* is defined as the log probability of a state being optimal. Bellman equations are derived relating Q* and V* to the reward and next state value.

Mathematical preliminaries in Automata

This document provides mathematical preliminaries for automata, including:
- Sets, functions, relations, graphs, and proof techniques like induction and proof by contradiction.
- It defines sets, set operations, functions, relations, graphs, trees, and binary trees.
- It also covers topics like equivalence relations, equivalence classes, Cartesian products, and power sets.

Improved Trainings of Wasserstein GANs (WGAN-GP)

This document summarizes improved training methods for Wasserstein GANs (WGANs). It begins with an overview of GANs and their limitations, such as gradient vanishing. It then introduces WGANs, which use the Wasserstein distance instead of Jensen-Shannon divergence to provide more meaningful gradients during training. However, weight clipping used in WGANs limits the function space and can cause optimization difficulties. The document proposes using gradient penalty instead of weight clipping to enforce a Lipschitz constraint. It also suggests sampling from an estimated optimal coupling rather than independently sampling real and generated samples to better match theory. Experimental results show the gradient penalty approach improves stability and performance of WGANs on image generation tasks.

Formal systems introduction

This document contains lecture slides on formal systems and computation from a course on IST Studies taught by George Pasparakis. It introduces key concepts like formal languages, symbols and alphabets, different types of automata including finite automata, pushdown automata, and Turing machines. It also discusses the CPU components involved in computation like input memory, output memory, program memory, and temporary memory. Mathematical preliminaries covered include sets, functions, relations, graphs, and proof techniques like induction and proof by contradiction.

Random Forest

The document provides an overview of random forests, including the random forest recipe, why random forests work, and ramifications of random forests. The random forest recipe involves drawing bootstrap samples to grow trees, randomly selecting features at each split, and aggregating predictions. Random forests work by decorrelating trees, which reduces variance and leads to lower prediction error compared to individual trees. Ramifications discussed include using out-of-bag samples to estimate generalization error and calculating variable importance.

Policy Gradient Theorem

The document summarizes the policy gradient theorem, which provides a way to perform policy improvement in reinforcement learning using gradient ascent on the expected returns with respect to the policy parameters. It begins by motivating policy gradients as a way to do policy improvement when the action space is large or continuous. It then defines the necessary notation, expected returns objective function, and discounted state visitation measure. The main part of the document proves the policy gradient theorem, which expresses the policy gradient as an expectation over the discounted state visitation measure and action-value function. It notes that in practice the action-value function must be estimated, and proves the compatible function approximation theorem, which ensures the policy gradient is computed correctly when using an estimated action-value

Seminar - Similarity Joins in SQL (performance and semantic joins)

seminar regarding similarity joins both on performance size and on advanced methods for semantic joins

Algorithm Design and Complexity - Course 7

The document discusses algorithms for graphs, including breadth-first search (BFS) and depth-first search (DFS). BFS uses a queue to traverse nodes level-by-level from a starting node, computing the shortest path. DFS uses a stack, exploring as far as possible along each branch before backtracking, and computes discovery and finish times for nodes. Both algorithms color nodes white, gray, black to track explored status and maintain predecessor pointers to reconstruct paths. Common graph representations like adjacency lists and matrices are also covered.

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable...

slide for "REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models", NIPS 2017.

QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...

QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...The Statistical and Applied Mathematical Sciences Institute

This talk was presented as part of the Trends and Advances in Monte Carlo Sampling Algorithms Workshop.Information in the Weights

The document discusses information theory concepts like entropy, joint entropy, conditional entropy, and mutual information. It then discusses how these concepts relate to generalization in deep learning models. Specifically, it explains that the PAC-Bayesian bound is data-dependent, so models with high VC dimension can still generalize if the data is clean, resulting in low KL divergence between the prior and posterior distributions.

Information in the Weights

The document discusses information theory concepts like entropy, joint entropy, conditional entropy, and mutual information. It then discusses how these concepts relate to generalization in deep learning models. Specifically, it explains that the PAC-Bayesian bound is data-dependent, so models with high VC dimension can still generalize if the data is clean, resulting in low KL divergence between the prior and posterior distributions.

Typing quantum superpositions and measurement

We propose a way to unify two approaches of non-cloning in quantum lambda-calculi. The first approach is to forbid duplicating variables, while the second is to consider all lambda-terms as algebraic-linear functions. We illustrate this idea by defining a quantum extension of first-order simply-typed lambda-calculus, where the type is linear on superposition, while allows cloning base vectors. In addition, we provide an interpretation of the calculus where superposed types are interpreted as vector spaces and non-superposed types as their basis.
Slides of LNCS 10687:281-293 paper (TPNC 2017). Full paper: https://doi.org/10.1007/978-3-319-71069-3_22

Minimizing cost in distributed multiquery processing applications

A brief overview of several methods to optimize data movement in distributed multi-query processing applications.

A new Perron-Frobenius theorem for nonnegative tensors

Based on the concept of dimensional partition we consider a general tensor spectral problem that includes all known tensor spectral problems as special cases. We formulate irreducibility and symmetry properties in terms of the dimensional partition and use the theory of multi-homogeneous order-preserving maps to derive a general and unifying Perron-Frobenius theorem for nonnegative tensors that either includes previous results of this kind or improves them by weakening the assumptions there considered.
Talk presented at SIAM Applied Linear Algebra conference Hong Kong 2018

QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...

QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...The Statistical and Applied Mathematical Sciences Institute

This talk was presented as part of the Trends and Advances in Monte Carlo Sampling Algorithms Workshop.Sets in discrete mathematics

Discrete Mathematics - Sets. ... He had defined a set as a collection of definite and distinguishable objects selected by the means of certain rules or description. Set theory forms the basis of several other fields of study like counting theory, relations, graph theory and finite state machines.

Wei Yang - 2015 - Sampling-based Alignment and Hierarchical Sub-sentential Al...

Wei Yang - 2015 - Sampling-based Alignment and Hierarchical Sub-sentential Al...Association for Computational Linguistics

This paper evaluates different alignment methods for Chinese to Japanese patent translation, including sampling-based alignment and hierarchical sub-sentential alignment. Experimental results show this combined method significantly reduces training time compared to traditional GIZA++ alignment, with translation quality remaining steady. Specifically, using this method, training time was reduced to just 57 minutes while maintaining comparable BLEU scores, representing a five-fold decrease compared to GIZA++. The paper concludes this approach can effectively accelerate statistical machine translation system development for patent translation tasks.140106 isaim-okayama

The document presents a condition under which unbounded unions of languages can be learned from positive data using refinement operators. Specifically, it introduces two theorems:
1) Theorem 1 states that a concept class (C,R,L) is learnable if it admits a refinement operator satisfying properties [A-1] to [A-3].
2) Theorem 2 (the contribution of the paper) states that the union concept class (C*,R*,L) is learnable if (C,R,L) admits a refinement operator satisfying [A-1] to [A-3] and additional properties [C-1] and [C-2]. This allows learning of unbounded unions of languages.

Link analysis

This document provides an overview of link analysis and summarizes several common approaches:
1. Early approaches calculated site popularity based on incoming and outgoing links, but had limitations in accounting for link importance. HITS introduced the concepts of hubs and authorities, where authoritative sites receive links from important hubs.
2. PageRank assigns weight based on the rank of parent sites, inversely proportional to their outdegree. It models the probability of a random web surfer being on a page.
3. Link analysis approaches like HITS and PageRank have limitations, such as failing to account for dangling nodes with no links. Modifications include adding a small probability of randomly restarting from any page.

Presentation

The document discusses language models and n-gram language models used in machine translation. A language model calculates probabilities of sentences without seeing the source sentence. It breaks down the probability of a sentence into a product of conditional probabilities of each word given previous words using the chain rule. However, directly calculating these probabilities results in many probabilities being zero. Therefore, n-gram language models condition each word on the previous n-1 words to address this issue. The document also discusses smoothing techniques like linear interpolation and Witten-Bell to address data sparsity issues with n-gram probabilities.

On the Jensen-Shannon symmetrization of distances relying on abstract means

Slides for the paper
On the Jensen–Shannon Symmetrization of Distances Relying on Abstract Means
https://www.mdpi.com/1099-4300/21/5/485

Unit II Problem Solving Methods in AI K.sundar,AP/CSE,VEC

This document discusses various problem solving methods in artificial intelligence, including informed search techniques like heuristics, local search algorithms, and constraint satisfaction problems. It provides examples of applying heuristics, A*, and alpha-beta pruning to problems like the 8-puzzle and game playing. Constraint satisfaction problems are formalized as variables with domains and constraints, and can be solved with backtracking search.

Nodal Domain Theorem for the p-Laplacian on Graphs and the Related Multiway C...

We consider the p-Laplacian on discrete graphs, a nonlinear operator that generalizes the standard graph Laplacian (obtained for p=2). We consider a set of variational eigenvalues of this operator and analyze the nodal domain count of the corresponding eigenfunctions. In particular, we show that the famous Courant’s nodal domain theorem for the linear Laplacian carries over almost unchanged to the nonlinear case. Moreover, we use the nodal domains to prove a higher-order Cheeger inequality that relates the k-way graph cut to the k-th variational eigenvalue of the p-Laplacian

Paper introduction to Combinatorial Optimization on Graphs of Bounded Treewidth

This slides introduced the paper: H. L. Bodlaender and a. M. C. a. Koster, “Combinatorial Optimization on Graphs of Bounded Treewidth,” Comput. J., vol. 51, no. 3, pp. 255–269, Nov. 2007.

Refining Bayesian Data Analysis Methods for Use with Longer Waveforms

This document discusses refining Bayesian data analysis methods for analyzing longer gravitational waveforms detected by advanced gravitational wave detectors. The primary objective is to investigate increased parallelization of the nested sampling algorithm and the secondary objective is to develop a variable resolution algorithm to improve handling of template waveforms. Nested sampling is described as a method to efficiently calculate the evidence integral for model selection. Parallelizing nested sampling by running it simultaneously with different random seeds and combining the results is proposed. A variable resolution function is motivated to adapt the resolution of template waveforms based on their complexity, focusing more resources on more complex sections.

[Book Reading] 機械翻訳 - Section 7 No.1

The document discusses various methods for optimization in machine translation decoding, including loss minimization, minimum error rate training (MERT), softmax loss, max margin loss, pairwise ranking optimization, and minimum Bayes risk. It covers challenges like non-differentiable error functions and vast search spaces, and how different methods address these challenges through techniques like Powell's method, gradient-based methods, and sentence-level BLEU approximations.

Henk de Vries

Henk de Vries is the director of Feadship, a leading Dutch superyacht builder. He joined the family business in 1987 after working as a management consultant. While initially reluctant, he was impressed by the entrepreneurial spirit of the shareholders. Under his leadership as CEO, Feadship has grown significantly. In the 2000s, Feadship briefly built semi-custom yachts but found it was not well-suited to their expertise in custom builds. After the financial crisis, de Vries led a redefinition of Feadship's build processes to become more efficient while maintaining their focus on custom yachts. Their new structured approach includes regular milestone reviews and process audits.

Algorithm Design and Complexity - Course 7

The document discusses algorithms for graphs, including breadth-first search (BFS) and depth-first search (DFS). BFS uses a queue to traverse nodes level-by-level from a starting node, computing the shortest path. DFS uses a stack, exploring as far as possible along each branch before backtracking, and computes discovery and finish times for nodes. Both algorithms color nodes white, gray, black to track explored status and maintain predecessor pointers to reconstruct paths. Common graph representations like adjacency lists and matrices are also covered.

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable...

slide for "REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models", NIPS 2017.

QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...

QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...The Statistical and Applied Mathematical Sciences Institute

This talk was presented as part of the Trends and Advances in Monte Carlo Sampling Algorithms Workshop.Information in the Weights

The document discusses information theory concepts like entropy, joint entropy, conditional entropy, and mutual information. It then discusses how these concepts relate to generalization in deep learning models. Specifically, it explains that the PAC-Bayesian bound is data-dependent, so models with high VC dimension can still generalize if the data is clean, resulting in low KL divergence between the prior and posterior distributions.

Typing quantum superpositions and measurement

We propose a way to unify two approaches of non-cloning in quantum lambda-calculi. The first approach is to forbid duplicating variables, while the second is to consider all lambda-terms as algebraic-linear functions. We illustrate this idea by defining a quantum extension of first-order simply-typed lambda-calculus, where the type is linear on superposition, while allows cloning base vectors. In addition, we provide an interpretation of the calculus where superposed types are interpreted as vector spaces and non-superposed types as their basis.
Slides of LNCS 10687:281-293 paper (TPNC 2017). Full paper: https://doi.org/10.1007/978-3-319-71069-3_22

Minimizing cost in distributed multiquery processing applications

A brief overview of several methods to optimize data movement in distributed multi-query processing applications.

A new Perron-Frobenius theorem for nonnegative tensors

Based on the concept of dimensional partition we consider a general tensor spectral problem that includes all known tensor spectral problems as special cases. We formulate irreducibility and symmetry properties in terms of the dimensional partition and use the theory of multi-homogeneous order-preserving maps to derive a general and unifying Perron-Frobenius theorem for nonnegative tensors that either includes previous results of this kind or improves them by weakening the assumptions there considered.
Talk presented at SIAM Applied Linear Algebra conference Hong Kong 2018

Sets in discrete mathematics

Discrete Mathematics - Sets. ... He had defined a set as a collection of definite and distinguishable objects selected by the means of certain rules or description. Set theory forms the basis of several other fields of study like counting theory, relations, graph theory and finite state machines.

Wei Yang - 2015 - Sampling-based Alignment and Hierarchical Sub-sentential Al...

Wei Yang - 2015 - Sampling-based Alignment and Hierarchical Sub-sentential Al...Association for Computational Linguistics

This paper evaluates different alignment methods for Chinese to Japanese patent translation, including sampling-based alignment and hierarchical sub-sentential alignment. Experimental results show this combined method significantly reduces training time compared to traditional GIZA++ alignment, with translation quality remaining steady. Specifically, using this method, training time was reduced to just 57 minutes while maintaining comparable BLEU scores, representing a five-fold decrease compared to GIZA++. The paper concludes this approach can effectively accelerate statistical machine translation system development for patent translation tasks.140106 isaim-okayama

The document presents a condition under which unbounded unions of languages can be learned from positive data using refinement operators. Specifically, it introduces two theorems:
1) Theorem 1 states that a concept class (C,R,L) is learnable if it admits a refinement operator satisfying properties [A-1] to [A-3].
2) Theorem 2 (the contribution of the paper) states that the union concept class (C*,R*,L) is learnable if (C,R,L) admits a refinement operator satisfying [A-1] to [A-3] and additional properties [C-1] and [C-2]. This allows learning of unbounded unions of languages.

Link analysis

This document provides an overview of link analysis and summarizes several common approaches:
1. Early approaches calculated site popularity based on incoming and outgoing links, but had limitations in accounting for link importance. HITS introduced the concepts of hubs and authorities, where authoritative sites receive links from important hubs.
2. PageRank assigns weight based on the rank of parent sites, inversely proportional to their outdegree. It models the probability of a random web surfer being on a page.
3. Link analysis approaches like HITS and PageRank have limitations, such as failing to account for dangling nodes with no links. Modifications include adding a small probability of randomly restarting from any page.

Presentation

The document discusses language models and n-gram language models used in machine translation. A language model calculates probabilities of sentences without seeing the source sentence. It breaks down the probability of a sentence into a product of conditional probabilities of each word given previous words using the chain rule. However, directly calculating these probabilities results in many probabilities being zero. Therefore, n-gram language models condition each word on the previous n-1 words to address this issue. The document also discusses smoothing techniques like linear interpolation and Witten-Bell to address data sparsity issues with n-gram probabilities.

On the Jensen-Shannon symmetrization of distances relying on abstract means

Slides for the paper
On the Jensen–Shannon Symmetrization of Distances Relying on Abstract Means
https://www.mdpi.com/1099-4300/21/5/485

Unit II Problem Solving Methods in AI K.sundar,AP/CSE,VEC

This document discusses various problem solving methods in artificial intelligence, including informed search techniques like heuristics, local search algorithms, and constraint satisfaction problems. It provides examples of applying heuristics, A*, and alpha-beta pruning to problems like the 8-puzzle and game playing. Constraint satisfaction problems are formalized as variables with domains and constraints, and can be solved with backtracking search.

Nodal Domain Theorem for the p-Laplacian on Graphs and the Related Multiway C...

We consider the p-Laplacian on discrete graphs, a nonlinear operator that generalizes the standard graph Laplacian (obtained for p=2). We consider a set of variational eigenvalues of this operator and analyze the nodal domain count of the corresponding eigenfunctions. In particular, we show that the famous Courant’s nodal domain theorem for the linear Laplacian carries over almost unchanged to the nonlinear case. Moreover, we use the nodal domains to prove a higher-order Cheeger inequality that relates the k-way graph cut to the k-th variational eigenvalue of the p-Laplacian

Paper introduction to Combinatorial Optimization on Graphs of Bounded Treewidth

This slides introduced the paper: H. L. Bodlaender and a. M. C. a. Koster, “Combinatorial Optimization on Graphs of Bounded Treewidth,” Comput. J., vol. 51, no. 3, pp. 255–269, Nov. 2007.

Refining Bayesian Data Analysis Methods for Use with Longer Waveforms

This document discusses refining Bayesian data analysis methods for analyzing longer gravitational waveforms detected by advanced gravitational wave detectors. The primary objective is to investigate increased parallelization of the nested sampling algorithm and the secondary objective is to develop a variable resolution algorithm to improve handling of template waveforms. Nested sampling is described as a method to efficiently calculate the evidence integral for model selection. Parallelizing nested sampling by running it simultaneously with different random seeds and combining the results is proposed. A variable resolution function is motivated to adapt the resolution of template waveforms based on their complexity, focusing more resources on more complex sections.

Algorithm Design and Complexity - Course 7

Algorithm Design and Complexity - Course 7

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable...

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable...

QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...

QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...

Information in the Weights

Information in the Weights

Information in the Weights

Information in the Weights

Typing quantum superpositions and measurement

Typing quantum superpositions and measurement

Minimizing cost in distributed multiquery processing applications

Minimizing cost in distributed multiquery processing applications

A new Perron-Frobenius theorem for nonnegative tensors

A new Perron-Frobenius theorem for nonnegative tensors

QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...

QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...

Sets in discrete mathematics

Sets in discrete mathematics

Wei Yang - 2015 - Sampling-based Alignment and Hierarchical Sub-sentential Al...

Wei Yang - 2015 - Sampling-based Alignment and Hierarchical Sub-sentential Al...

140106 isaim-okayama

140106 isaim-okayama

Link analysis

Link analysis

Presentation

Presentation

On the Jensen-Shannon symmetrization of distances relying on abstract means

On the Jensen-Shannon symmetrization of distances relying on abstract means

Unit II Problem Solving Methods in AI K.sundar,AP/CSE,VEC

Unit II Problem Solving Methods in AI K.sundar,AP/CSE,VEC

Nodal Domain Theorem for the p-Laplacian on Graphs and the Related Multiway C...

Nodal Domain Theorem for the p-Laplacian on Graphs and the Related Multiway C...

Paper introduction to Combinatorial Optimization on Graphs of Bounded Treewidth

Paper introduction to Combinatorial Optimization on Graphs of Bounded Treewidth

Refining Bayesian Data Analysis Methods for Use with Longer Waveforms

Refining Bayesian Data Analysis Methods for Use with Longer Waveforms

[Book Reading] 機械翻訳 - Section 7 No.1

The document discusses various methods for optimization in machine translation decoding, including loss minimization, minimum error rate training (MERT), softmax loss, max margin loss, pairwise ranking optimization, and minimum Bayes risk. It covers challenges like non-differentiable error functions and vast search spaces, and how different methods address these challenges through techniques like Powell's method, gradient-based methods, and sentence-level BLEU approximations.

Henk de Vries

Henk de Vries is the director of Feadship, a leading Dutch superyacht builder. He joined the family business in 1987 after working as a management consultant. While initially reluctant, he was impressed by the entrepreneurial spirit of the shareholders. Under his leadership as CEO, Feadship has grown significantly. In the 2000s, Feadship briefly built semi-custom yachts but found it was not well-suited to their expertise in custom builds. After the financial crisis, de Vries led a redefinition of Feadship's build processes to become more efficient while maintaining their focus on custom yachts. Their new structured approach includes regular milestone reviews and process audits.

[Book Reading] 機械翻訳 - Section 3 No.1

The document discusses methods for estimating the probability (P) that a sentence (e) is natural or grammatically correct using n-gram language models. It explains that n-gram models approximate P(e) by considering the probability of word sequences of length n rather than all preceding words. This helps address the problem of P(e) being estimated as 0 when e is not present in the training data. The document also covers smoothing techniques like linear interpolation and Witten-Bell smoothing that combine n-gram and (n-1)-gram probabilities to further address cases where n-gram probabilities are 0.

[Paper Introduction] Supervised Phrase Table Triangulation with Neural Word E...

[Paper Introduction] Supervised Phrase Table Triangulation with Neural Word E...NAIST Machine Translation Study Group

Paper Introduction,
"Supervised Phrase Table Triangulation with Neural Word Embeddings for Low-Resource Languages"
Tomer Levinboim and David Chiang[Paper Introduction] Bilingual word representations with monolingual quality ...

[Paper Introduction] Bilingual word representations with monolingual quality ...NAIST Machine Translation Study Group

1) The document discusses methods for creating bilingual word representations, which are vectors that represent words from two languages in a single vector space.
2) It presents an approach called Bilingual Skipgram that trains word representations by substituting words from one language to predict contexts in the other language.
3) Evaluation shows this approach achieves better performance on monolingual tasks compared to previous methods, while still performing well on cross-lingual tasks.[Paper Introduction] Evaluating MT Systems with Second Language Proficiency T...

[Paper Introduction] Evaluating MT Systems with Second Language Proficiency T...NAIST Machine Translation Study Group

This study evaluates machine translation systems using second language proficiency tests to measure human performance on tasks using machine-translated texts. The researchers had 320 Japanese junior high students answer multiple-choice questions based on conversations translated by 4 systems - Google Translate, Yahoo Translate, and two human translations, one with and one without context. They found that considering context was important for accurate translations, as the system that included context performed better. Scores on the proficiency tests agreed somewhat with automatic evaluation metrics but captured additional aspects of translation quality. The tests also proved robust to differences between test-takers. [Book Reading] 機械翻訳 - Section 2 No.2

This document discusses various automatic evaluation metrics for machine translation:
- BLEU evaluates matching n-grams between reference and translated texts but ignores position and favors shorter translations.
- METEOR explicitly matches words accounting for stem, synonym, and paraphrase matches. It aims for high precision and recall.
- RIBES uses rank correlation coefficients between reference and translation word order to evaluate language pairs where word-for-word matching is difficult.
- Statistical testing like bootstrapping is used to determine if differences in evaluation scores between systems are statistically significant.

[Paper Introduction] Translating into Morphologically Rich Languages with Syn...

[Paper Introduction] Translating into Morphologically Rich Languages with Syn...NAIST Machine Translation Study Group

Paper Introduction,
"Translating into Morphologically Rich Languages with Synthetic Phrases"
Victor Chahuneau, Eva Schlinger, Noah A. Smith, Chris Dyer (EMNLP2013)
[Paper Introduction] Training a Natural Language Generator From Unaligned Data

[Paper Introduction] Training a Natural Language Generator From Unaligned DataNAIST Machine Translation Study Group

The document summarizes a research paper on training a natural language generator from unaligned data. The paper proposes a novel method that integrates the data alignment step into the sentence planning process using deep syntactic trees and rule-based surface realization. This allows the system to learn from incomplete trees and capture long-range syntactic dependencies without requiring a separate alignment step. The method uses an A* search algorithm during sentence planning and is trained on a restaurant domain dataset to generate text from abstract representations, showing improvement over previous work.[Paper Introduction] Efficient top down btg parsing for machine translation p...

[Paper Introduction] Efficient top down btg parsing for machine translation p...NAIST Machine Translation Study Group

1) The document proposes an efficient top-down parsing algorithm for preordering source sentences in machine translation using bilexical grammar (BTG) trees. 2) Existing BTG-based preordering approaches are slow due to their use of CKY parsing and loss function calculations with time complexity of O(n^5). 3) The proposed approach uses an incremental top-down parsing algorithm with early updates and beam search, achieving time complexity of O(n^2) and making it 10-100 times faster than prior work. 4) Experimental results show the efficient approach provides better BLEU scores in machine translation compared to prior BTG preordering methods.TCR 20 Sharkwater

1) Sharks have existed for over 400 million years but their populations have declined by 90% due to human activity like shark finning.
2) Underwater photographer Rob Stewart documented the threats facing sharks, like finning, in his film Sharkwater. He aimed to educate viewers about sharks and encourage people to care about their extinction.
3) Stewart's film reveals that sharks play an important role in marine ecosystems and that the perception of sharks as dangerous man-eaters is false, with only 5 fatal attacks per year while millions of sharks are killed by humans.

[Paper Introduction] A Context-Aware Topic Model for Statistical Machine Tran...

[Paper Introduction] A Context-Aware Topic Model for Statistical Machine Tran...NAIST Machine Translation Study Group

The document presents a context-aware topic model (CATM) for statistical machine translation. CATM jointly models local sentence context and global document topics to improve lexical selection. It achieves the highest translation performance compared to models using only context or topics. The CATM is the first work to jointly learn both context and topic information for lexical selection in statistical machine translation.Diffuser soi-même sa recherche: quelques outils et stratégies pour l'étudiant...

Diffuser soi-même sa recherche: quelques outils et stratégies pour l'étudiant...Gabriel Dumouchel, Ph.D.

Cet atelier présente des stratégies et des outils qui peuvent servir à rendre autonome l'étudiant-chercheur en éducation dans la diffusion de ses recherches au cours de sa formation.La présence numérique de l'étudiant-chercheur en éducation: quelques outils p...

La présence numérique de l'étudiant-chercheur en éducation: quelques outils p...Gabriel Dumouchel, Ph.D.

À l'ère du Web 2.0, l'étudiant-chercheur en éducation a désormais accès à un arsenal d'outils conviviaux et puissants (ex : blogues) pour appuyer son traditionnel CV dans l'élaboration et la diffusion de son profil de futur chercheur. Il a ainsi l'opportunité de prendre en main facilement et à moindres coûts la création de sa présence numérique en recherche plutôt que de dépendre d’une institution pour ce faire. Car il arrive encore trop souvent que de nouveaux diplômés terminent leurs études armés principalement de leur CV, et que cette stratégie les handicape dans leur recherche d'emploi. Sans être la panacée, le fait de se créer une solide présence numérique en cours de formation peut contribuer à leur employabilité. Cette communication vise donc à leur proposer quelques pistes pour mettre en place, organiser et entretenir de manière pragmatique et abordable une telle présence par l'entremise d'une sélection d'outils offerts sur le Web, et de stratégies de positionnement à adopter.Zotero Standalone : un incontournable pour gérer vos références de recher...

Zotero Standalone : un incontournable pour gérer vos références de recher...Gabriel Dumouchel, Ph.D.

Le logiciel de gestion bibliographique Zotero Standalone offre aux chercheurs et étudiants universitaires en sciences de l’éducation un outil de qualité à la fois gratuit, simple, efficace et rapide. Que ce soit pour permettre aux utilisateurs de créer des références bibliographiques en un seul clic sur le Web, de les gérer, de les employer dans leurs rédactions ou encore de les partager avec leurs collègues, Zotero Standalone représente un formidable allié en formation comme en recherche. Cet atelier présentera notamment comment un chercheur ou un étudiant-chercheur en éducation peut employer cet outil afin de concevoir et d’organiser judicieusement sa bibliothèque de références et de l’utiliser de manière optimale avec les fonctionnalités d’insertion dans un logiciel de traitement de texte.LEGRIS Presentation

Legris Industries group is a diversified industrial company with 4 divisions: Industrial Fluids, Clay Building Materials, Logistics, and Extrusion. It has 26 industrial sites across 30 countries, €670 million in turnover, and 4,200 employees from 30 nationalities. The Industrial Fluids division, which includes Legris SA, is the #1 worldwide in low and medium pressure instant connectors with €209 million in revenues. Legris SA has 10 production and logistics sites, 1,683 employees across 25 commercial subsidiaries, and provides instant fitting solutions for pneumatic, hydraulic, fuel, and automotive applications across various industries.

PARKER Presentation

Parker Hannifin is a global leader in motion and control technologies that partners with customers to increase their productivity and profitability. Parker's Win Strategy has goals of premier customer service, financial performance, and profitable growth with strategies like delivery of quality parts on time, value-added services, and acquisitions and globalization. Parker has over 55,000 employees, 8,400 distributors worldwide, and serves over 417,000 customers across 9 technologies and 7 business groups operating 123 divisions and 299 plants worldwide.

Comment gérer son identité numérique de chercheur ou d’étudiant-chercheur en ...

Comment gérer son identité numérique de chercheur ou d’étudiant-chercheur en ...Gabriel Dumouchel, Ph.D.

PowerPoint de ma partie de la conférence du 31 mai 2013 présentée avec Patrick Giroux (UQAC) intitulée "L’identité numérique des chercheurs et étudiants-chercheurs en éducation : définition, importance et stratégies de gestion 2.0".
Résumé:
Dans le monde hyperconnecté dans lequel nous vivons, le chercheur et l'étudiant-chercheur en éducation doivent s'adapter à une nouvelle réalité qui est d'exister tant dans le milieu universitaire que sur le Web. En effet, les traces laissées entre autres par la recherche et l'enseignement sur l'autoroute de l'information façonnent leur identité numérique. Or, se créer et gérer une telle identité de manière réfléchie et stratégique est désormais essentiel.
Cette communication vise donc d'une part à présenter et définir l'identité numérique en mettant l'accent sur différents types de traces qui la composent et sur diverses raisons qui justifient que l'on s'en préoccupe. D'autre part, elle présentera des stratégies et différents outils susceptibles d'aider les chercheurs et étudiants-chercheurs en éducation à gérer et promouvoir leur identité numérique.La promotion de l’identité numérique des chercheurs et des étudiants-cherc...

La promotion de l’identité numérique des chercheurs et des étudiants-cherc...Gabriel Dumouchel, Ph.D.

À l’ère du Web 2.0, tant le chercheur que l’étudiant-chercheur en éducation ont désormais accès à un arsenal d’outils conviviaux et puissants pour appuyer leur traditionnel CV dans l’élaboration et la diffusion de leur profil de chercheur actuel ou futur. Ils ont ainsi l’opportunité de prendre en main facilement et à moindres coûts la création de leur présence numérique en recherche plutôt que de dépendre d’une institution pour ce faire. Sans être la panacée, le fait de se créer une solide présence numérique en cours de formation ou lors d’une carrière en recherche peut contribuer à l’employabilité de l’un et à la diffusion des travaux de l’autre. Cette communication vise donc à présenter quelques pistes pour mettre en place, organiser et entretenir de manière pragmatique et abordable une telle présence par l’intermédiaire d’une sélection d’outils offerts sur le Web, et de stratégies de positionnement à adopter.[Book Reading] 機械翻訳 - Section 7 No.1

[Book Reading] 機械翻訳 - Section 7 No.1

Henk de Vries

Henk de Vries

MBYRussia

MBYRussia

[Book Reading] 機械翻訳 - Section 3 No.1

[Book Reading] 機械翻訳 - Section 3 No.1

[Paper Introduction] Supervised Phrase Table Triangulation with Neural Word E...

[Paper Introduction] Supervised Phrase Table Triangulation with Neural Word E...

[Paper Introduction] Bilingual word representations with monolingual quality ...

[Paper Introduction] Bilingual word representations with monolingual quality ...

[Paper Introduction] Evaluating MT Systems with Second Language Proficiency T...

[Paper Introduction] Evaluating MT Systems with Second Language Proficiency T...

[Book Reading] 機械翻訳 - Section 2 No.2

[Book Reading] 機械翻訳 - Section 2 No.2

[Paper Introduction] Translating into Morphologically Rich Languages with Syn...

[Paper Introduction] Translating into Morphologically Rich Languages with Syn...

[Paper Introduction] Training a Natural Language Generator From Unaligned Data

[Paper Introduction] Training a Natural Language Generator From Unaligned Data

[Paper Introduction] Efficient top down btg parsing for machine translation p...

[Paper Introduction] Efficient top down btg parsing for machine translation p...

TCR 20 Sharkwater

TCR 20 Sharkwater

[Paper Introduction] A Context-Aware Topic Model for Statistical Machine Tran...

[Paper Introduction] A Context-Aware Topic Model for Statistical Machine Tran...

Diffuser soi-même sa recherche: quelques outils et stratégies pour l'étudiant...

Diffuser soi-même sa recherche: quelques outils et stratégies pour l'étudiant...

La présence numérique de l'étudiant-chercheur en éducation: quelques outils p...

La présence numérique de l'étudiant-chercheur en éducation: quelques outils p...

Zotero Standalone : un incontournable pour gérer vos références de recher...

Zotero Standalone : un incontournable pour gérer vos références de recher...

LEGRIS Presentation

LEGRIS Presentation

PARKER Presentation

PARKER Presentation

Comment gérer son identité numérique de chercheur ou d’étudiant-chercheur en ...

Comment gérer son identité numérique de chercheur ou d’étudiant-chercheur en ...

La promotion de l’identité numérique des chercheurs et des étudiants-cherc...

La promotion de l’identité numérique des chercheurs et des étudiants-cherc...

Future features for openCypher: Schema, Constraints, Subqueries, Configurable...

Presented at the First openCypher Implementers Meeting in Walldorf, Germany, February 2017 @ http://www.opencypher.org/blog/2017/03/31/first-ocim-blog/

G6 m5-b-lesson 7-t

This document describes a lesson on determining distance on the coordinate plane. Students will use absolute value to find the lengths of line segments between integer coordinates. They will recognize when segments are vertical or horizontal based on shared x- or y-coordinates. For vertical or horizontal segments, distance is calculated by subtracting or adding the absolute values of the different coordinates. Students practice finding distances and representing their work in a table with a column showing the calculation.

Paper study: Attention, learn to solve routing problems!

This is the paper from ICLR 2019
The authors aim to use the attention mechanism (especially Transfomer) to solve routing problem including TSP.

Metrics for generativemodels

Generative models aim to learn a data distribution p(x|θ) from training samples. Three common ways to measure similarity between the model distribution p and the data distribution q are:
1) Kullback-Leibler (KL) divergence, which is used in maximum likelihood estimation.
2) Jensen-Shannon (JS) divergence, which is minimized during training of generative adversarial networks (GANs).
3) Optimal transport (OT) distance, such as the 1-Wasserstein distance, which provides a smooth measure of similarity and can be applied in the form of Wasserstein GANs.

Approximate Nearest Neighbour in Higher Dimensions

This document discusses approximate nearest neighbor (ANN) search in high dimensional spaces. It begins by introducing the ANN problem and noting the "curse of dimensionality" that makes exact searches inefficient in high dimensions. It then discusses constructing a (1+ε)-approximate NN data structure for the Hamming cube using locality sensitive hashing (LSH). The data structure uses O(dn + n1+ρ) space and O(nρ) hash probes per query, where ρ depends on sensitivity properties of the hash family. The document also discusses using LSH for ANN search in Euclidean spaces by projecting points to random lines, using multiple projections to amplify probabilities of nearby points hashing to the same value.

Optimum Engineering Design - Day 2b. Classical Optimization methods

This document provides an overview of an optimization methods course, including its objectives, prerequisites, and materials. The course covers topics such as linear programming, nonlinear programming, and mixed integer programming problems. It also includes mathematical preliminaries on topics like convex sets and functions, gradients, Hessians, and Taylor series expansions. Methods for solving systems of linear equations and examples are presented.

Biological sequences analysis

A review of two alignment-free methods for sequence comparison. In this presentation two alignment-free methods are studied:
- "Similarity analysis of DNA sequences based on LZ complexity and dynamic programming algorithm" by Guo et al.
- "Alignment-free comparison of genome sequences by a new numerical characterization" by Huang et al.

lecture 17

The document discusses interval trees and breadth-first search (BFS) algorithms. Interval trees are used to maintain a set of intervals and efficiently find overlapping intervals given a query. BFS is a graph search algorithm that explores all neighboring vertices of a starting node before moving to neighbors of neighbors. BFS builds a breadth-first tree and calculates the shortest path distances from the source node in O(V+E) time and space.

Prestation_ClydeShen

This document discusses graph colouring and graph dynamical systems. It begins with an overview of graph theory concepts like graphs, graph colouring, and the graph colouring problem. It then discusses deterministic finite automata and introduces graph-cellular automata as a type of graph dynamical system. Specific examples of graph-cellular automata on linear graphs, circle graphs, tree graphs, wheel graphs, and Peterson graphs are analyzed. The results show that linear graphs, circle graphs, and tree graphs reach a stable coloring, while wheel graphs and Peterson graphs result in a loop.

Lash

The document summarizes the LASH algorithm for mining sequential patterns from sequence data with hierarchies. LASH extends traditional sequential pattern mining to handle hierarchies among items. It first defines how sequences can be generalized based on item hierarchies. It then partitions the sequence database based on the most frequent items and mines generalized patterns within each partition. Key steps include identifying relevant items, generalizing sequences, and representing equivalent sequences compactly to efficiently find all frequent generalized sequences satisfying maximum length and gap constraints.

Algorithm to count number of disjoint paths

A simple algorithm I devised to enumerate the disjoint paths between a pair of vertices in a given graph. This algorithm was devised as a part of my course-work for Masters [Tech.] at IIT-Banaras Hindu University.

is anyone_interest_in_auto-encoding_variational-bayes

Deep generative model 중 하나인 VAE의 Framework은 컴퓨터 비전, 자연어 처리 등 머신러닝의 전반에서 generative model의 변화를 가져왔다.
VAE를 처음 접하는 연구자들을 위해 대부분의 VAE tutorial은 구현을 목적으로 Neural Network구조와 Loss function에 초점을 맞추고 있다. 본 세미나는 Variational Inference 관점에서 Auto-encoding variational bayes에 나오는 수식들을 살펴보고자 한다. 본 수식들이 구현에서는 어떻게 적용되는지도 살펴보고자 한다.

Vehicle Routing Problem using PSO (Particle Swarm Optimization)

The document describes using particle swarm optimization to solve a capacitated vehicle routing problem. The objective is to minimize the total distance traveled by a fleet of vehicles to service customers while meeting vehicle capacity constraints. It provides details on initializing particle positions and velocities, calculating fitness based on route distance, and iteratively updating particles to find the optimal solution. The proposed approach is demonstrated on an example problem with 10 customers, 4 vehicles of capacity 200, and distances given between all locations. Pseudocode and equations are included for implementing the particle swarm optimization algorithm to find high-quality routes for the vehicle routing problem.

Paper Study: Melding the data decision pipeline

Melding the data decision pipeline: Decision-Focused Learning for Combinatorial Optimization from AAAI2019.
Derive the math equation from myself and match the same result as two mentioned CMU papers [Donti et. al. 2017, Amos et. al. 2017] while applying the same derivation procedure.

Secure Domination in graphs

The document discusses weighted secure domination in graphs. It begins by defining domination number and weighted domination number. It proposes a greedy algorithm that provides a 1 + log(n) approximation for weighted domination number. The algorithm works by iteratively selecting the unselected vertex with the minimum ratio of weight to number of uncovered neighbors. This achieves an approximation ratio of H(n), which is at most 1 + log(n). The algorithm runs in polynomial time.

02-alignment.pdf

The document provides an overview of sequence alignment, including:
- The task of sequence alignment is to determine the correspondences between substrings in sequences that maximize a similarity score.
- Alignment allows inference of homology and function based on sequence similarity.
- Key issues include variable sequence lengths, small matching regions, and modeling substitutions and gaps.
- Dynamic programming is used to find optimal global and local alignments in quadratic time by solving subproblems and reusing results.

openCypher: Further Developments on Path Pattern Queries (Regular Path Queries)

Presented at the Second openCypher Implementers Group Meeting in July 2017 @ http://www.opencypher.org/ocig/2017/07/06/ocig2/

Single source shortes path in dag

The document describes the single-source shortest paths algorithm for directed acyclic graphs (DAGs). It involves topologically sorting the vertices of the DAG, initializing distances from the source vertex s, and then relaxing edges in topologically sorted order. This guarantees that when a vertex u is relaxed, the shortest path distances from s to its neighbors will be accurate. The algorithm runs in O(V+E) time. It is used to find critical paths in PERT charts by finding the longest path after negating or reversing edge weights.

DFA minimization algorithms in map reduce

This document summarizes the key aspects of the author's master's thesis which proposes algorithms for minimizing deterministic finite automata (DFAs) in the Map-Reduce framework. It introduces DFA minimization and existing sequential algorithms. It then presents related work on parallel DFA minimization and proposes new Map-Reduce algorithms based on Moore's and Hopcroft's sequential algorithms. Finally, it analyzes the communication and computational costs of the proposed parallel algorithms.

20180831 riemannian representation learning

This document provides an overview of hierarchical representation with hyperbolic geometry. It introduces hyperbolic space as an alternative to Euclidean space for embedding symbolic and hierarchical data. Key points covered include: (1) the limitations of Euclidean embedding for graph structures, (2) definitions of hyperbolic space and the Poincare disk model, (3) optimization techniques for gradient descent in hyperbolic space including calculating gradients and using retractions, and (4) simple toy experiments demonstrating optimization in hyperbolic space.

Future features for openCypher: Schema, Constraints, Subqueries, Configurable...

Future features for openCypher: Schema, Constraints, Subqueries, Configurable...

G6 m5-b-lesson 7-t

G6 m5-b-lesson 7-t

Paper study: Attention, learn to solve routing problems!

Paper study: Attention, learn to solve routing problems!

Metrics for generativemodels

Metrics for generativemodels

Approximate Nearest Neighbour in Higher Dimensions

Approximate Nearest Neighbour in Higher Dimensions

Optimum Engineering Design - Day 2b. Classical Optimization methods

Optimum Engineering Design - Day 2b. Classical Optimization methods

Biological sequences analysis

Biological sequences analysis

lecture 17

lecture 17

Prestation_ClydeShen

Prestation_ClydeShen

Lash

Lash

Algorithm to count number of disjoint paths

Algorithm to count number of disjoint paths

is anyone_interest_in_auto-encoding_variational-bayes

is anyone_interest_in_auto-encoding_variational-bayes

Vehicle Routing Problem using PSO (Particle Swarm Optimization)

Vehicle Routing Problem using PSO (Particle Swarm Optimization)

Paper Study: Melding the data decision pipeline

Paper Study: Melding the data decision pipeline

Secure Domination in graphs

Secure Domination in graphs

02-alignment.pdf

02-alignment.pdf

openCypher: Further Developments on Path Pattern Queries (Regular Path Queries)

openCypher: Further Developments on Path Pattern Queries (Regular Path Queries)

Single source shortes path in dag

Single source shortes path in dag

DFA minimization algorithms in map reduce

DFA minimization algorithms in map reduce

20180831 riemannian representation learning

20180831 riemannian representation learning

BRIC_2024_2024-06-06-11:30-haunschild_archival_version.pdf

These are the slides of my presentation at BRIC 2024 about global science overlay maps using OpenAlex.

The Intersection between Competition and Data Privacy – COLANGELO – June 2024...

The Intersection between Competition and Data Privacy – COLANGELO – June 2024...OECD Directorate for Financial and Enterprise Affairs

This presentation by Professor Giuseppe Colangelo, Jean Monnet Professor of European Innovation Policy, was made during the discussion “The Intersection between Competition and Data Privacy” held at the 143rd meeting of the OECD Competition Committee on 13 June 2024. More papers and presentations on the topic can be found at oe.cd/ibcdp.
This presentation was uploaded with the author’s consent.
Artificial Intelligence, Data and Competition – OECD – June 2024 OECD discussion

Artificial Intelligence, Data and Competition – OECD – June 2024 OECD discussionOECD Directorate for Financial and Enterprise Affairs

This presentation by OECD, OECD Secretariat, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
The Intersection between Competition and Data Privacy – KEMP – June 2024 OECD...

The Intersection between Competition and Data Privacy – KEMP – June 2024 OECD...OECD Directorate for Financial and Enterprise Affairs

This presentation by Katharine Kemp, Associate Professor at the Faculty of Law & Justice at UNSW Sydney, was made during the discussion “The Intersection between Competition and Data Privacy” held at the 143rd meeting of the OECD Competition Committee on 13 June 2024. More papers and presentations on the topic can be found at oe.cd/ibcdp.
This presentation was uploaded with the author’s consent.
Carrer goals.pptx and their importance in real life

Career goals serve as a roadmap for individuals, guiding them toward achieving long-term professional aspirations and personal fulfillment. Establishing clear career goals enables professionals to focus their efforts on developing specific skills, gaining relevant experience, and making strategic decisions that align with their desired career trajectory. By setting both short-term and long-term objectives, individuals can systematically track their progress, make necessary adjustments, and stay motivated. Short-term goals often include acquiring new qualifications, mastering particular competencies, or securing a specific role, while long-term goals might encompass reaching executive positions, becoming industry experts, or launching entrepreneurial ventures.
Moreover, having well-defined career goals fosters a sense of purpose and direction, enhancing job satisfaction and overall productivity. It encourages continuous learning and adaptation, as professionals remain attuned to industry trends and evolving job market demands. Career goals also facilitate better time management and resource allocation, as individuals prioritize tasks and opportunities that advance their professional growth. In addition, articulating career goals can aid in networking and mentorship, as it allows individuals to communicate their aspirations clearly to potential mentors, colleagues, and employers, thereby opening doors to valuable guidance and support. Ultimately, career goals are integral to personal and professional development, driving individuals toward sustained success and fulfillment in their chosen fields.

Gamify it until you make it Improving Agile Development and Operations with ...

So many challenges, so little time. While we’re busy developing software and keeping it operational, we also need to sharpen the saw, but how? Gamification can be a way to look at how you’re doing and find out where to improve. It’s a great way to have everyone involved and get the best out of people.
In this presentation, Ben Linders will show how playing games with the DevOps coaching cards can help to explore your current development and deployment (DevOps) practices and decide as a team what to improve or experiment with.
The games that we play are based on an engagement model. Instead of imposing change, the games enable people to pull in ideas for change and apply those in a way that best suits their collective needs.
By playing games, you can learn from each other. Teams can use games, exercises, and coaching cards to discuss values, principles, and practices, and share their experiences and learnings.
Different game formats can be used to share experiences on DevOps principles and practices and explore how they can be applied effectively. This presentation provides an overview of playing formats and will inspire you to come up with your own formats.

Disaster Management project for holidays homework and other uses

It talks about disaster management in a helful way.

Why Psychological Safety Matters for Software Teams - ACE 2024 - Ben Linders.pdf

Psychological safety in teams is important; team members must feel safe and able to communicate and collaborate effectively to deliver value. It’s also necessary to build long-lasting teams since things will happen and relationships will be strained.
But, how safe is a team? How can we determine if there are any factors that make the team unsafe or have an impact on the team’s culture?
In this mini-workshop, we’ll play games for psychological safety and team culture utilizing a deck of coaching cards, The Psychological Safety Cards. We will learn how to use gamification to gain a better understanding of what’s going on in teams. Individuals share what they have learned from working in teams, what has impacted the team’s safety and culture, and what has led to positive change.
Different game formats will be played in groups in parallel. Examples are an ice-breaker to get people talking about psychological safety, a constellation where people take positions about aspects of psychological safety in their team or organization, and collaborative card games where people work together to create an environment that fosters psychological safety.

Artificial Intelligence, Data and Competition – SCHREPEL – June 2024 OECD dis...

Artificial Intelligence, Data and Competition – SCHREPEL – June 2024 OECD dis...OECD Directorate for Financial and Enterprise Affairs

This presentation by Thibault Schrepel, Associate Professor of Law at Vrije Universiteit Amsterdam University, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
Using-Presentation-Software-to-the-Fullf.pptx

Slides on 7 Commonly Used Presentation Softwares; thier usage and features.

Pro-competitive Industrial Policy – OECD – June 2024 OECD discussion

Pro-competitive Industrial Policy – OECD – June 2024 OECD discussionOECD Directorate for Financial and Enterprise Affairs

This presentation by OECD, OECD Secretariat, was made during the discussion “Pro-competitive Industrial Policy” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/pcip.
This presentation was uploaded with the author’s consent.
Artificial Intelligence, Data and Competition – ČORBA – June 2024 OECD discus...

Artificial Intelligence, Data and Competition – ČORBA – June 2024 OECD discus...OECD Directorate for Financial and Enterprise Affairs

This presentation by Juraj Čorba, Chair of OECD Working Party on Artificial Intelligence Governance (AIGO), was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
The Intersection between Competition and Data Privacy – OECD – June 2024 OECD...

The Intersection between Competition and Data Privacy – OECD – June 2024 OECD...OECD Directorate for Financial and Enterprise Affairs

This presentation by OECD, OECD Secretariat, was made during the discussion “The Intersection between Competition and Data Privacy” held at the 143rd meeting of the OECD Competition Committee on 13 June 2024. More papers and presentations on the topic can be found at oe.cd/ibcdp.
This presentation was uploaded with the author’s consent.
ServiceNow CIS-ITSM Exam Dumps & Questions [2024]

• For a full set of 530+ questions. Go to
https://skillcertpro.com/product/servicenow-cis-itsm-exam-questions/
• SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
• It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
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• SkillCertPro assures 100% pass guarantee in first attempt.

IEEE CIS Webinar Sustainable futures.pdf

The importance of sustainable and efficient computational practices in artificial intelligence (AI) and deep learning has become increasingly critical. This webinar focuses on the intersection of sustainability and AI, highlighting the significance of energy-efficient deep learning, innovative randomization techniques in neural networks, the potential of reservoir computing, and the cutting-edge realm of neuromorphic computing. This webinar aims to connect theoretical knowledge with practical applications and provide insights into how these innovative approaches can lead to more robust, efficient, and environmentally conscious AI systems.
Webinar Speaker: Prof. Claudio Gallicchio, Assistant Professor, University of Pisa
Claudio Gallicchio is an Assistant Professor at the Department of Computer Science of the University of Pisa, Italy. His research involves merging concepts from Deep Learning, Dynamical Systems, and Randomized Neural Systems, and he has co-authored over 100 scientific publications on the subject. He is the founder of the IEEE CIS Task Force on Reservoir Computing, and the co-founder and chair of the IEEE Task Force on Randomization-based Neural Networks and Learning Systems. He is an associate editor of IEEE Transactions on Neural Networks and Learning Systems (TNNLS).

The Intersection between Competition and Data Privacy – CAPEL – June 2024 OEC...

The Intersection between Competition and Data Privacy – CAPEL – June 2024 OEC...OECD Directorate for Financial and Enterprise Affairs

This presentation by Tim Capel, Director of the UK Information Commissioner’s Office Legal Service, was made during the discussion “The Intersection between Competition and Data Privacy” held at the 143rd meeting of the OECD Competition Committee on 13 June 2024. More papers and presentations on the topic can be found at oe.cd/ibcdp.
This presentation was uploaded with the author’s consent.
Artificial Intelligence, Data and Competition – LIM – June 2024 OECD discussion

Artificial Intelligence, Data and Competition – LIM – June 2024 OECD discussionOECD Directorate for Financial and Enterprise Affairs

This presentation by Yong Lim, Professor of Economic Law at Seoul National University School of Law, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
原版制作贝德福特大学毕业证（bedfordhire毕业证）硕士文凭原版一模一样

原版一模一样【微信：741003700 】【贝德福特大学毕业证（bedfordhire毕业证）硕士文凭】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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Pro-competitive Industrial Policy – LANE – June 2024 OECD discussion

Pro-competitive Industrial Policy – LANE – June 2024 OECD discussionOECD Directorate for Financial and Enterprise Affairs

This presentation by Nathaniel Lane, Associate Professor in Economics at Oxford University, was made during the discussion “Pro-competitive Industrial Policy” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/pcip.
This presentation was uploaded with the author’s consent.
BRIC_2024_2024-06-06-11:30-haunschild_archival_version.pdf

BRIC_2024_2024-06-06-11:30-haunschild_archival_version.pdf

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原版制作贝德福特大学毕业证（bedfordhire毕业证）硕士文凭原版一模一样

原版制作贝德福特大学毕业证（bedfordhire毕业证）硕士文凭原版一模一样

怎么办理(lincoln学位证书)英国林肯大学毕业证文凭学位证书原版一模一样

怎么办理(lincoln学位证书)英国林肯大学毕业证文凭学位证书原版一模一样

Pro-competitive Industrial Policy – LANE – June 2024 OECD discussion

Pro-competitive Industrial Policy – LANE – June 2024 OECD discussion

- 1. Graph Structure ・Use search graph in phrase-based model ・At weighted acyclic directed graph G < Ф,V,E,s,g,𝐴> Ф : phrase pair sets Ф=feature vector h(・)・weight 𝜔 V: vertex ≡ partial hypotheses E:edges ≡ weight of route E ⊆ V×V× Ф×A A: weight sets
- 2. Graph Structure • out(𝑣)= 𝑣 = 𝑒 ∈ 𝐸|tail(𝑒) : edge sets which go out from vertex 𝑣 • in(𝑣) = 𝑣 = 𝑒 ∈ 𝐸|head(𝑒) : edge sets which head to vertex 𝑣 ->Phrase pairs are linked by <out(𝑣), in(𝑣)> At figure 5.8, phrase pair <へ行った, I went to> is linked by out(𝑣) = <-----,0,<s>> and in(𝑣)=<--・・・,9,went to> 𝑣 𝑣
- 3. Graph Structure • If Ѱ=(𝑒1, 𝑒1,…, 𝑒l): rout from start to any vertexs, head(𝑒k)=tail(𝑒k+1), then Source language phrase sets: 𝑘=1 𝑙 𝑓(∅(𝑒 𝑘)) ≡ 𝑓(Ѱ) Target language phrase sets: 𝑒(∅ 𝑒1 ), … , 𝑒(∅ 𝑒𝑙 ≡ 𝑒(Ѱ) Route weight: 𝜔(Ѱ)= 𝑘=1 𝑙 𝜔(𝑒 𝑘)
- 4. Graph Structure • In Fig.5.8, for the route -> the parallel of word sets of source language 「行った」「へ」「領事館」is “He went to the consulate” Start <行った,He went> <へ,to> <領事館, the consulate>
- 5. Semiring • set R equipped with two binary operations addition“ + ” and multiplication “ × ” • Associative: a+(b+c)=(a+b)+c, a×(b×c)=(a×b)×c • Commutative: a+b=b+a • Distributional: a×(b+c)=(a×b)+(a×c) • Additive inverse, multiplicative inverse 0+a=a+0=a; 1×a=a×1=a; 0×a=a×0=0 are not defined
- 6. Semiring • In Table 5.1, tropical semiring is used to solve maximization problem for route weight in decoder A ⊕ ⊗ 𝟎 𝟏 Tropical 𝑅−∞ ∞ max + ー∞ 0
- 7. Semiring • In weight directed graph G, for a rout from starting point to ending point of source language input f is Ѱ= 𝑒1, 𝑒1,…, 𝑒l • Score of Ѱ = product of partial routes 𝜔(Ѱ)=⊗ 𝑘=1 𝜔(𝑒 𝑘) -> Problem which maximize this score is max⊗𝜔(𝑒)= ⊕⊗𝜔(𝑒) A ⊕ ⊗ 𝟎 𝟏 Tropical 𝑅−∞ ∞ max + ー∞ 0
- 8. Semiring • In Fig.5.7,line 11 Q(𝑣′ , 𝑗′′ +1,𝑒′ 𝑠 𝑒′′ 𝑠)←max Q(𝑣′ , 𝑗′′ +1,𝑒′ 𝑠 𝑒′′ 𝑠), Q(𝑣, 𝑗, 𝑒′ 𝑒′′ )+𝑠 𝑑 + 𝑠∅ + 𝑠𝑙𝑚 additive operation ⊕ is implemented for each vertex tail(e)=s of G • As semiring sastifies distributional feature -> weight 𝜔(𝑣)of any vertexs 𝑣 ∈V is ⊕⊗𝜔(𝑒)=⊕ 𝑒∈𝑖𝑛(𝑣) 𝜔(𝑒)⊗ 𝜔(𝑡𝑎𝑖𝑙(𝑒))
- 9. Semiring • Forward-backward algorithm for finding maximum of route weight in graph structure • topological order(G): list of vertexs of graph G which arranged in topological order • 𝛼, 𝛽: external variable
- 10. Semiring FORWARD(G) • 𝑣 ∈ topological order(G), e∈in(𝑣) 𝜔 = 𝜔(𝑒)⊗ 𝛼(𝑡𝑎𝑖𝑙(𝑒)) 𝛼 𝑣 = 𝛼(𝑣)⊕ 𝜔 Start tail(e) 𝜔(e) 𝜔 = 𝜔(e) ⊗ 𝛼(𝑡𝑎𝑖𝑙(𝑒))
- 11. Semiring BACKWARD(G) • 𝑣 ∈ inversetopological order(G), e∈ out(𝑣) 𝜔 = 𝜔(𝑒)⊗ 𝛽(ℎ𝑒𝑎𝑑(𝑒)) 𝛽 𝑣 = 𝛽(𝑣)⊕ 𝜔 Goal 𝜔(e) 𝜔 = 𝜔(e)⊗ 𝛽(ℎ𝑒𝑎𝑑(𝑒)) head(e)
- 12. Semiring In problem which choose the optimum translation from search space expressed by weighted directed graph G Tropical semiring + Forward algorithm ->Viterbi semiring
- 13. k-best • Besides forward-backward algorithm, k-best algorithm is used to optimize route weight • Dijkstra’s algorithm: for single source shortest path problem • Eppstein’s algorithm: for heaping multiple paths efficiently
- 14. k-best • Assume problem satisfies Tropical semiring and backward algorithm • Calculate and choose max (weight 𝛽(𝑣)) • Fig.5.10 algorithm ・cand: priority queue ・< 𝑣, s>: partial route ・< 𝑣′ ,𝑠′ >: partial route whose vertex 𝑣′ = 𝑣 and edge 𝑠′ = tail 𝑒 = 𝑒 ∈out(𝑣) ・D: set of < 𝑣′ ,𝑠′ >
- 15. k-best • k=1: Initialized cand • Optimize weight of partial route and whole route Whole route D cand optimal get out < 𝑣, s>,register D Choose 𝑣′ = 𝑣 and 𝑒′ = e ∈out(𝑣) insert to cand heap 𝛽(・) to get optimal k time
- 16. Limitation of Search Space • If search space is big ->any sort can be forgiven ->calculation amount of decode algorithm become massive ->limitation is necessary: ・Distortion limit, constraint ・Reordering limit, constraint
- 17. Distortion Constraint • Upper limit setting d for distance between phrase pair ∅ 𝑘and∅ 𝑘−1: start 𝑘 − end 𝑘−1 ≤d The purpose is making model score small if model distorted lead to penalty become big For language pair which do not have big sort, distortion constraint reach good efficiency If d=0: no skip, translate from left to right smoothly ->monotone translation
- 18. Distortion Constraint • Constraint for case when have partial phrases do not reach the ending point 𝑗: position of the first phrase of source language start 𝑘: the first position of translated phrase If ( 𝑗 < start 𝑘), add end 𝑘 − 𝑗 ≤d ・IBM Constraint 𝑗 𝑠𝑡𝑎𝑟𝑡 𝑘 𝑒𝑛𝑑 𝑘・・・ ∅ 𝑘 phrase No need to exam
- 19. Beam Search ・Prune disused partial hypothesis and pay attention only partial hypothesis with high score for computational reduction ・Group of vertexs of search graph and prune partial hypothesis which has low score
- 20. Beam Search ・Group of vertexs of search graph and prune partial hypothesis which has low score Partial hypothesis pruned Partial hypothesis chose
- 21. Beam Search Some kinds of grouping: - Cover vector grouping - Radix grouping - Beam width pruning - Histogram pruning
- 22. Heuristic Function • Prevent partial hypothesis which has not been translated yet from pruning • Give predicted score for the rout and learn by A* search so that rout score get the maximum • ->can reduce search error
- 23. Pre-reordering Method Translation between languages which has significantly different grammatical structure • Pre-reordering rule • Pre-reordering model • Pre-reordering learning
- 24. Pre-reordering Rule • Based on tree from syntactic analysis, reorder to target language word order • Head-driven phrase structure grammar(HPSG)’s rule: - Syntactic anlysis - Move the subjects back
- 25. Pre-reordering Model • Source languages must have syntactic analysis tool and morphological analysis tool • Bilingual data are necessary • Probability value of pre-reordering patterns obtained will be estimated by maximum- likelihood estimation(MLE) • Choose the suitable pre-reordering patterns based on reordering part of speech from morphological analysis, or clustering word class
- 26. Pre-reordering Learning • For language pairs without any syntactic analysis tools and morphological analysis tools • Provisional tree structure automatically generated from syntactic analysis result • Divide tree factors to 2 labels: reordering label [X],and no-reordering label <X> • Use linear ordering problem(LOP) to formulate reordering model to find the approximate solution and build the parse tree

- 句に基づく機会翻訳のモデルを構築するには、グラフ構造を利用することも考えられます。図5.8,各部分仮説を頂点とし、フレーズ∅が割り当てられる重み付き非周期有向グラフをグラフの各辺のラベルとして表現します。Фはフレーズペアの集合、Aは各辺に割り当てられる重みの集合とします。Vは頂点の集合であり、Eは辺の集合です。
- out(v)を頂点vから出ていく辺の集合とし、In(v)を頂点vへ向かう辺の集合とする。このとき、各フレーズペアが表された辺により結ばれる。図5.8では、フレーズペアphrase pair <へ行った, I went to＞がで結ばれることが分かりました。
- 開始頂点から任意の頂点v∈𝑉へのある経路Ѱは辺の系列Ѱ=( 𝑒 1 , 𝑒 1 ,…, 𝑒 l )として表すことができます。このとき、Ѱの各辺のフレーズペアの原言語側の集合 𝑘=1 𝑙 𝑓 (∅( 𝑒 𝑘 )) ≡ 𝑓 (Ѱ) は、原言語の入力文の部分単語列であり、目的言語側を連結した単語列 𝑒 (∅ 𝑒 1 ),…, 𝑒 (∅ 𝑒 𝑙 ≡ 𝑒 (Ѱ)は、翻訳された目的言語の文の接頭辞に相当する。経路の重みは、各辺の重みの和として表されます。
- 「行った、He went」、「へ,to」、「領事館、consulate」という三つのペアで構造される経路により、原言語の単語の集合「」「」「」に対応する目的言語の翻訳文は”He went to the consulate”
- 半環は集合に対する加算、乗算という二つの二項演算で定義される。結合性、可換性、分配性という性質を持っている。ただし、加法逆元、および乗法逆元が定義されないのです。
- 重み付き有向グラフGにおいて、開始頂点から終了頂点までの経路はѰ= 𝑒 1 , 𝑒 1 ,…, 𝑒 l 。経路のスコアは各部分経路のスコアの積であり、
- 半環は分配性を満たすため、図5.7の行11で、加法演算に対応する⊕がGの各頂点について実行されるため、ある頂点vの重みを𝜔(𝑣)とすると
- 前向き後ろ向きアルゴリズムをグラフ構造へ一般化したものと考えられます。ここで、topological order(G)はグラフGの頂点を位相的順序に並び替えられたリストとします。𝛼,𝛽:外部変数
- 位相的順序の逆順のリスト
- 重み付き有向グラフGで表された探索空間から最適な翻訳を選択する問題は、前向きアルゴリズムでTropical半環を用いる経路を記憶することはビタビ半環といいます。
- 最適な翻訳を求めるのには、前向きアルゴリズムの他、最短経路問題の解を求めるダイクストラ法、複数の経路をヒープで効率よくするエプシュタイン法があります
- Cand:優先度付きキュー、<v,s>部分経路 𝑣 ′ =𝑣 and edge 𝑠 ′ =tail 𝑒 =𝑒∈out(𝑣)だけを列挙
- 全体経路から部分経路<v,s>を取り出し、Dへ登録、頂点vの各辺e∈out(𝑣) を列挙して、新しい< 𝑣 ′ , 𝑠 ′ >をキューcandへ挿入する。𝛽(・)が最大になるようねheapでキューcandの各要素の順序を決定する。部分経路のmax𝛽から全体経路の𝛽の最大を求めるアルゴリズムです。
- 探索空間が膨大だと、任意の並び変えを許してしまい、デコードのアルゴリズムの計算量が大きくなってしまう。そこで、歪み制限、制約あるいは並べ替え制限、制約などを加え、現実的な計算量を削減する必要がある。
- ・原言語の連続するフレーズペア ∅ 𝑘 および ∅ 𝑘−1 の距離に対し上限値dを設定し、歪みモデルによるペナルティが大きい場合には、モデルのスコアが小さくなる。・また、原言語の最初の単語をスキップして残りの単語を全てスキップなしで単調に翻訳した場合、最後にスキップされた最初の単語へと戻るために、歪み制約を満たすことができない。そこで、飛びすぎた句があったとしても、戻ることを可能とするように、
- ・原言語の連続するフレーズペア ∅ 𝑘 および ∅ 𝑘−1 の距離に対し上限値dを設定し、歪みモデルによるペナルティが大きい場合には、モデルのスコアが小さくなる。・また、原言語の最初の単語をスキップして残りの単語を全てスキップなしで単調に翻訳した場合、最後にスキップされた最初の単語へと戻るために、歪み制約を満たすことができない。そこで、飛びすぎた句があったとしても、戻ることを可能とするように、さらに制約を加える。例えば、まだ翻訳されていない、最初の原言語の単語の位置をjとする。つぎにk翻訳されるフレーズペアの最初の位置がjよりも右にあったとき(j<start),最後の位置とjとの距離をd以内とする制約を加える。
- ビーム探索では、部分仮説を枝刈りし、高いスコアをもつ部分仮説のみに注目し、近似的に最大化のl問題を解く。ビーム探索では、類似した部分仮説、探索グラフの頂点をグループ化し、各グループごとにスコアの低い部分仮説を枝刈りする。
- ビーム探索では、部分仮説を枝刈りし、高いスコアをもつ部分仮説のみに注目し、近似的に最大化のl問題を解く。ビーム探索では、類似した部分仮説、探索グラフの頂点をグループ化し、各グループごとにスコアの低い部分仮説を枝刈りする。
- ビーム探索では、部分仮説を枝刈りし、高いスコアをもつ部分仮説のみに注目し、近似的に最大化のl問題を解く。ビーム探索では、類似した部分仮説、探索グラフの頂点をグループ化し、各グループごとにスコアの低い部分仮説を枝刈りする。
- 文法構造が大幅ｎ異なる言語間の翻訳を行う時に使う。
- HPSGの解析結果から得られる主辞の情報を利用し、主辞を後ろへ移動させると言ったルールー＞大幅に機械翻訳の性能を向上させている。
- 原言語と目的言語を精通するスペシャリストがいない場合、翻訳が実現不可能。これに対して、両言語の単語アライメントを利用して、並び替えルールを自動的に取得するモデル。 このモデルを使う条件として、原言語に対して構文解析器、あるいは形態素解析器が存在し、かつ対訳データが存在する。各パターンの確率値を最尤推定する。各パターンを適用するモデルを対数線形モデルにより実現し、エントロピー最大法によりそのパラメータを学習する。形態素解析結果から品詞の並び、あるいは、クラスタリングにより単語クラスの並びを用いて並び替えパターンを取得する
- 構文解析器から仮の構文解析木が自動的に生成され、を並び替えをしないラベル、並び替えをするラベルに分けられる。並び替えのモデルを線形順序付け問題として定式化し、近似解を見つけ、解析木を構築する