Beyond clicks dwell time for personalization
•
0 likes•261 views
Beyond clicks dwell time for personalization, Recsys'14の 論文紹介，http://www.hongliangjie.com/publications/recsys2014.pdf
Report
Share
Report
Share
Download to read offline
Recommended
[DL輪読会]GANとエネルギーベースモデル
This document discusses the connections between generative adversarial networks (GANs) and energy-based models (EBMs). It shows that GAN training can be interpreted as approximating maximum likelihood training of an EBM by replacing the intractable data distribution with a generator distribution. Specifically:
1. GANs train a discriminator to estimate the energy function of an EBM, with the generator minimizing that energy of its samples.
2. EBM training can be seen as alternatively updating the generator and sampling from it, in a manner similar to contrastive divergence for EBMs.
3. This perspective unifies GANs and EBMs, and suggests ways to combine their training procedures to leverage their respective advantages
【DL輪読会】NeRF-VAE: A Geometry Aware 3D Scene Generative Model
NeRF-VAE is a 3D scene generative model that combines Neural Radiance Fields (NeRF) and Generative Query Networks (GQN) with a variational autoencoder (VAE). It uses a NeRF decoder to generate novel views conditioned on a latent code. An encoder extracts latent codes from input views. During training, it maximizes the evidence lower bound to learn the latent space of scenes and allow for novel view synthesis. NeRF-VAE aims to generate photorealistic novel views of scenes by leveraging NeRF's view synthesis abilities within a generative model framework.
Variational AutoEncoder
1. The document discusses probabilistic modeling and variational inference. It introduces concepts like Bayes' rule, marginalization, and conditioning.
2. An equation for the evidence lower bound is derived, which decomposes the log likelihood of data into the Kullback-Leibler divergence between an approximate and true posterior plus an expected log likelihood term.
3. Variational autoencoders are discussed, where the approximate posterior is parameterized by a neural network and optimized to maximize the evidence lower bound. Latent variables are modeled as Gaussian distributions.
Megadata With Python and Hadoop
This document discusses processing large datasets with Python and Hadoop. It begins with an example of finding the highest temperature from a climate dataset using a map-reduce approach. Next, it provides code examples for implementing map-reduce in pure Python, with Hadoop Streaming, and with the Dumbo library. The document then discusses using Amazon Elastic MapReduce for running Hadoop jobs on AWS. It poses a question about how to implement breadth-first search as a map-reduce algorithm and ends with an example of using MongoDB's map-reduce functionality.
【DL輪読会】SUMO: Unbiased Estimation of Log Marginal Probability for Latent Varia...
The document proposes a method called SUMO (Stochastically Unbiased Marginalization Objective) for estimating log marginal probabilities in latent variable models. SUMO uses a Russian roulette estimator to obtain an unbiased estimate of the log marginal likelihood. This allows SUMO to provide an objective function for variational inference that converges to the log marginal likelihood as more samples are taken, avoiding the bias issues of methods like VAEs and IWAE. The paper outlines SUMO, compares it to existing methods, and demonstrates its effectiveness on density estimation tasks.
Semi vae memo (1)
This document discusses using variational autoencoders for semi-supervised learning. It presents the general variational formula for calculating the log likelihood of data, and derives lower bound formulas for semi-supervised models. Specifically, it shows lower bound formulas for predicting a semi-supervised value z given inputs x and y, and for predicting both z and a supervised value y given only x as input. The key ideas are using an encoder-decoder model with latent variables z and y, and optimizing an objective function that combines supervised and unsupervised loss terms.
Semi vae memo (2)
This document summarizes a research paper on semi-supervised learning with deep generative models. It presents the key formulas and derivations used in variational autoencoders (VAEs) and their extension to semi-supervised models. The proposed semi-supervised model has two lower bounds - one for labeled data that maximizes the likelihood of inputs given labels, and one for unlabeled data that maximizes the likelihood based on inferred labels. Experimental results show the model achieves better classification accuracy compared to supervised models as the number of labeled samples increases.
確率的推論と行動選択
This document discusses the relationship between control as inference, reinforcement learning, and active inference. It provides an overview of key concepts such as Markov decision processes (MDPs), partially observable MDPs (POMDPs), optimality variables, the evidence lower bound (ELBO), variational inference, and the free energy principle as applied to active inference. Control as inference frames reinforcement learning as probabilistic inference by defining a generative process and performing variational inference to find an optimal policy. Active inference uses the free energy principle and minimizes expected free energy to select actions that resolve uncertainty.
Recommended
[DL輪読会]GANとエネルギーベースモデル
This document discusses the connections between generative adversarial networks (GANs) and energy-based models (EBMs). It shows that GAN training can be interpreted as approximating maximum likelihood training of an EBM by replacing the intractable data distribution with a generator distribution. Specifically:
1. GANs train a discriminator to estimate the energy function of an EBM, with the generator minimizing that energy of its samples.
2. EBM training can be seen as alternatively updating the generator and sampling from it, in a manner similar to contrastive divergence for EBMs.
3. This perspective unifies GANs and EBMs, and suggests ways to combine their training procedures to leverage their respective advantages
【DL輪読会】NeRF-VAE: A Geometry Aware 3D Scene Generative Model
NeRF-VAE is a 3D scene generative model that combines Neural Radiance Fields (NeRF) and Generative Query Networks (GQN) with a variational autoencoder (VAE). It uses a NeRF decoder to generate novel views conditioned on a latent code. An encoder extracts latent codes from input views. During training, it maximizes the evidence lower bound to learn the latent space of scenes and allow for novel view synthesis. NeRF-VAE aims to generate photorealistic novel views of scenes by leveraging NeRF's view synthesis abilities within a generative model framework.
Variational AutoEncoder
1. The document discusses probabilistic modeling and variational inference. It introduces concepts like Bayes' rule, marginalization, and conditioning.
2. An equation for the evidence lower bound is derived, which decomposes the log likelihood of data into the Kullback-Leibler divergence between an approximate and true posterior plus an expected log likelihood term.
3. Variational autoencoders are discussed, where the approximate posterior is parameterized by a neural network and optimized to maximize the evidence lower bound. Latent variables are modeled as Gaussian distributions.
Megadata With Python and Hadoop
This document discusses processing large datasets with Python and Hadoop. It begins with an example of finding the highest temperature from a climate dataset using a map-reduce approach. Next, it provides code examples for implementing map-reduce in pure Python, with Hadoop Streaming, and with the Dumbo library. The document then discusses using Amazon Elastic MapReduce for running Hadoop jobs on AWS. It poses a question about how to implement breadth-first search as a map-reduce algorithm and ends with an example of using MongoDB's map-reduce functionality.
【DL輪読会】SUMO: Unbiased Estimation of Log Marginal Probability for Latent Varia...
The document proposes a method called SUMO (Stochastically Unbiased Marginalization Objective) for estimating log marginal probabilities in latent variable models. SUMO uses a Russian roulette estimator to obtain an unbiased estimate of the log marginal likelihood. This allows SUMO to provide an objective function for variational inference that converges to the log marginal likelihood as more samples are taken, avoiding the bias issues of methods like VAEs and IWAE. The paper outlines SUMO, compares it to existing methods, and demonstrates its effectiveness on density estimation tasks.
Semi vae memo (1)
This document discusses using variational autoencoders for semi-supervised learning. It presents the general variational formula for calculating the log likelihood of data, and derives lower bound formulas for semi-supervised models. Specifically, it shows lower bound formulas for predicting a semi-supervised value z given inputs x and y, and for predicting both z and a supervised value y given only x as input. The key ideas are using an encoder-decoder model with latent variables z and y, and optimizing an objective function that combines supervised and unsupervised loss terms.
Semi vae memo (2)
This document summarizes a research paper on semi-supervised learning with deep generative models. It presents the key formulas and derivations used in variational autoencoders (VAEs) and their extension to semi-supervised models. The proposed semi-supervised model has two lower bounds - one for labeled data that maximizes the likelihood of inputs given labels, and one for unlabeled data that maximizes the likelihood based on inferred labels. Experimental results show the model achieves better classification accuracy compared to supervised models as the number of labeled samples increases.
確率的推論と行動選択
This document discusses the relationship between control as inference, reinforcement learning, and active inference. It provides an overview of key concepts such as Markov decision processes (MDPs), partially observable MDPs (POMDPs), optimality variables, the evidence lower bound (ELBO), variational inference, and the free energy principle as applied to active inference. Control as inference frames reinforcement learning as probabilistic inference by defining a generative process and performing variational inference to find an optimal policy. Active inference uses the free energy principle and minimizes expected free energy to select actions that resolve uncertainty.
La R Users Group Survey Of R Graphics
A survey of data visualization functions and packages in R. In particular, I discuss three approaches for data visualization in R: (i) the built-in base graphics functions, (ii) the ggplot2 package, and (iii) the lattice package. I also discuss some methods for visualizing large data sets.
Quantum Machine Learning and QEM for Gaussian mixture models (Alessandro Luongo)
Quantum Machine Learning and QEM for Gaussian mixture models (Alessandro Luongo)MeetupDataScienceRoma
#ICML2020 Special Meetup: Quantum & Graph Machine Learning (Web Meetup)
Primo talk: quantum machine learning (Alessandro Luongo)深層生成モデルを用いたマルチモーダルデータの半教師あり学習
This document discusses several semi-supervised deep generative models for multimodal data, including the Semi-Supervised Multimodal Variational AutoEncoder (SS-MVAE), Semi-Supervised Hierarchical Multimodal Variational AutoEncoder (SS-HMVAE), and their training procedures. The SS-MVAE extends the Joint Multimodal Variational Autoencoder (JMVAE) to semi-supervised learning. The SS-HMVAE introduces auxiliary variables to model dependencies between modalities more flexibly. Both models maximize a variational lower bound with supervised and unsupervised objectives. The document provides technical details of the generative processes, variational approximations, and optimization of these semi-supervised deep generative models.
Irs gan doc
This document discusses the connection between generative adversarial networks (GANs) and inverse reinforcement learning (IRL). It shows that the objectives of GAN discriminators and IRL cost functions are equivalent, and GAN generators are equivalent to the IRL sampler objective plus a constant term. The derivative of the IRL cost function with respect to the cost parameters is also equivalent to the derivative of the GAN discriminator objective. Therefore, GANs can be used to perform IRL by training the discriminator to estimate the cost function and the generator to produce sample trajectories.
Chris hill rps_postgis_threeoutoffouraintbad_20150505_1
- PostGIS allows spatial and attribute querying together, advanced joins, aggregation functions, scripting in SQL for repeatability, good raster/vector integration, and linking to other languages like Python and R.
- It may take longer than desktop GIS for single tasks but enables automation through SQL scripts.
- The document discusses fuzzy polygon matching, measuring closest distances, creating simple transects with translation, rotation, and directionality, and provides examples of PostGIS functions like ST_Intersects and ST_Distance.
ggplot2: An Extensible Platform for Publication-quality Graphics
Talk given at the Symposium on Data Science and Statistics in Bellevue, Washington, May 29 - June 1, 2019, organized by the American Statistical Association and Interface Foundation of North America.
Quantum espresso G Vector distributon
This document describes the process of distributing reciprocal space grid points (G-vectors) across multiple processors for parallel computation in density functional theory (DFT) calculations. It involves 4 steps:
1) Initializing FFT descriptors and allocating data across processors
2) Mapping G-vectors to processors by iterating through grid points and checking if the vector satisfies cutoff criteria
3) Counting the number of G-vectors assigned to each processor
4) Sorting and distributing the G-vectors to optimize load balancing across processors
RBootcamp Day 4
This document summarizes key concepts from Day 4 of an R bootcamp, including:
- Using qplot() and ggplot() to create basic scatterplots and add elements like titles, colors, and smoothing lines
- The structure of ggplot2 graphs using data, aesthetics, geometries, scales, and other elements
- Techniques for dealing with overplotting like jittering, binning, and faceting
- Customizing graphs using themes, color palettes, and different coordinate systems
- Recreating an example of Minard's graph showing Napoleon's 1812 march and losses using ggplot2
Context-Aware Recommender System Based on Boolean Matrix Factorisation
In this work we propose and study an approach for collaborative filtering, which is based on Boolean matrix factorisation and exploits additional (context) information about users and items. To avoid similarity loss in case of Boolean representation we use an adjusted type of projection of a target user to the obtained factor space.
We have compared the proposed method with SVD-based approach on the MovieLens dataset. The experiments demonstrate that the proposed method has better MAE and Precision and comparable Recall and F-measure. We also report an increase of quality in the context information presence.
08 - 25 Jan - Divide and Conquer
This document discusses divide and conquer algorithms for multiplying integers and finding the kth smallest element. It describes how to multiply integers by dividing them into left and right halves, combining results. Finding the kth smallest element involves choosing a pivot, partitioning around it, and recursively searching halves until the kth element is found. Both algorithms take O(n log n) time by dividing the problem size by a constant factor at each step.
module1
The document contains code for a VBA macro that copies rows of data from sheet1 to sheet2. It defines variables to store values from 10 columns in sheet1. It then uses nested For loops to iterate through rows 1 to 10000 in sheet1, copy the values from each row to sheet2, and increment a reference line each time to paste to the next row.
Gan
This document provides an introduction to generative adversarial networks (GANs). It includes examples of GAN applications, an overview of how GANs work with generators and discriminators, the loss functions used in GAN training, and the global optimality conditions for GAN training. It explains that the global minimum is achieved when the generated distribution pz matches the real data distribution pdata, resulting in a value of -log4 for the loss function.
Tower of hanoi
The document discusses the Tower of Hanoi problem. It contains the following key points:
1) The Tower of Hanoi problem involves moving disks of different sizes between three towers, where the rules are that larger disks cannot be placed on top of smaller disks and only one disk can be moved at a time.
2) An algorithm for solving the Tower of Hanoi problem recursively moves all but one disk from the starting tower to an auxiliary tower, then moves the remaining disk to the destination tower, and finally moves the disks from the auxiliary tower to the destination tower.
3) The number of moves required to solve a Tower of Hanoi problem increases exponentially based on the number of disks,
Tower of hanoi
The document describes the Tower of Hanoi puzzle. It involves three pegs with disks of decreasing diameters stacked on one peg. The objective is to move the entire stack to another peg following two rules: only the top disk can be moved at a time, and a larger disk can never be placed on top of a smaller disk. The algorithm works recursively by breaking the problem down into moving all disks except the largest, moving the largest disk, and then moving the remaining disks.
k*NN2016
This document discusses k*-nearest neighbors (k*-NN), an improvement on the standard k-nearest neighbors (k-NN) algorithm. k*-NN aims to find an optimal value of k for k-NN by minimizing a loss function that trades off bias and variance. The k*-NN algorithm is presented, which calculates the optimal k and sample weights. Experimental results on several datasets show that k*-NN often outperforms standard k-NN and Nadaraya-Watson kernel regression, finding a better value of k through cross-validation.
Mate tarea - 2º
The document contains examples of algebraic expressions that can be factorized. Specifically, it lists 14 different expressions involving variables like x, y, z, a, b, c, m, n, and coefficients. The expressions include differences of terms, sums of terms, products of terms with common factors that can be pulled out, and expressions within parentheses that can be distributed and combined.
[DL輪読会]近年のエネルギーベースモデルの進展
1. The document discusses energy-based models (EBMs) and how they can be applied to classifiers. It introduces noise contrastive estimation and flow contrastive estimation as methods to train EBMs.
2. One paper presented trains energy-based models using flow contrastive estimation by passing data through a flow-based generator. This allows implicit modeling with EBMs.
3. Another paper argues that classifiers can be viewed as joint energy-based models over inputs and outputs, and should be treated as such. It introduces a method to train classifiers as EBMs using contrastive divergence.
Data Structure and Algorithms The Tower of Hanoi
The document describes the Tower of Hanoi puzzle, which involves moving disks of different sizes between three pegs according to rules of only moving one disk at a time and never placing a larger disk on top of a smaller one. It provides an algorithm and recursive solution for solving the puzzle by moving disks from the source peg to the auxiliary peg and then to the destination peg. The number of minimum moves needed to solve the puzzle for n disks is 2^n - 1. For example, 4 disks requires 15 moves.
19 bcs2241 daa_3.3
This document contains code and algorithms to find all occurrences of a pattern string P in a given text string S. It includes pseudocode that uses the Knuth-Morris-Pratt algorithm to compute a prefix function to match the pattern by skipping characters in the text. The code implements this algorithm to search for a pattern in a text and print the index of any matches found.
PHP Cheat Sheet
This PHP cheat sheet summarizes the most commonly-used language features. It is ideal for those new to learning this language.
A neural attention model for sentence summarization
A neural attention model for sentence summarization, EMNLP'15 の論文紹介．http://aclweb.org/anthology/D15-1044
Brief survey of data-to-text systems
Data-to-Textシステムに関する簡単なSurvey．特に，Data-to-Textの構成要素である，Signal AnalysisとSurface Realizationにフォーカスしています．
More Related Content
What's hot
La R Users Group Survey Of R Graphics
A survey of data visualization functions and packages in R. In particular, I discuss three approaches for data visualization in R: (i) the built-in base graphics functions, (ii) the ggplot2 package, and (iii) the lattice package. I also discuss some methods for visualizing large data sets.
Quantum Machine Learning and QEM for Gaussian mixture models (Alessandro Luongo)
Quantum Machine Learning and QEM for Gaussian mixture models (Alessandro Luongo)MeetupDataScienceRoma
#ICML2020 Special Meetup: Quantum & Graph Machine Learning (Web Meetup)
Primo talk: quantum machine learning (Alessandro Luongo)深層生成モデルを用いたマルチモーダルデータの半教師あり学習
This document discusses several semi-supervised deep generative models for multimodal data, including the Semi-Supervised Multimodal Variational AutoEncoder (SS-MVAE), Semi-Supervised Hierarchical Multimodal Variational AutoEncoder (SS-HMVAE), and their training procedures. The SS-MVAE extends the Joint Multimodal Variational Autoencoder (JMVAE) to semi-supervised learning. The SS-HMVAE introduces auxiliary variables to model dependencies between modalities more flexibly. Both models maximize a variational lower bound with supervised and unsupervised objectives. The document provides technical details of the generative processes, variational approximations, and optimization of these semi-supervised deep generative models.
Irs gan doc
This document discusses the connection between generative adversarial networks (GANs) and inverse reinforcement learning (IRL). It shows that the objectives of GAN discriminators and IRL cost functions are equivalent, and GAN generators are equivalent to the IRL sampler objective plus a constant term. The derivative of the IRL cost function with respect to the cost parameters is also equivalent to the derivative of the GAN discriminator objective. Therefore, GANs can be used to perform IRL by training the discriminator to estimate the cost function and the generator to produce sample trajectories.
Chris hill rps_postgis_threeoutoffouraintbad_20150505_1
- PostGIS allows spatial and attribute querying together, advanced joins, aggregation functions, scripting in SQL for repeatability, good raster/vector integration, and linking to other languages like Python and R.
- It may take longer than desktop GIS for single tasks but enables automation through SQL scripts.
- The document discusses fuzzy polygon matching, measuring closest distances, creating simple transects with translation, rotation, and directionality, and provides examples of PostGIS functions like ST_Intersects and ST_Distance.
ggplot2: An Extensible Platform for Publication-quality Graphics
Talk given at the Symposium on Data Science and Statistics in Bellevue, Washington, May 29 - June 1, 2019, organized by the American Statistical Association and Interface Foundation of North America.
Quantum espresso G Vector distributon
This document describes the process of distributing reciprocal space grid points (G-vectors) across multiple processors for parallel computation in density functional theory (DFT) calculations. It involves 4 steps:
1) Initializing FFT descriptors and allocating data across processors
2) Mapping G-vectors to processors by iterating through grid points and checking if the vector satisfies cutoff criteria
3) Counting the number of G-vectors assigned to each processor
4) Sorting and distributing the G-vectors to optimize load balancing across processors
RBootcamp Day 4
This document summarizes key concepts from Day 4 of an R bootcamp, including:
- Using qplot() and ggplot() to create basic scatterplots and add elements like titles, colors, and smoothing lines
- The structure of ggplot2 graphs using data, aesthetics, geometries, scales, and other elements
- Techniques for dealing with overplotting like jittering, binning, and faceting
- Customizing graphs using themes, color palettes, and different coordinate systems
- Recreating an example of Minard's graph showing Napoleon's 1812 march and losses using ggplot2
Context-Aware Recommender System Based on Boolean Matrix Factorisation
In this work we propose and study an approach for collaborative filtering, which is based on Boolean matrix factorisation and exploits additional (context) information about users and items. To avoid similarity loss in case of Boolean representation we use an adjusted type of projection of a target user to the obtained factor space.
We have compared the proposed method with SVD-based approach on the MovieLens dataset. The experiments demonstrate that the proposed method has better MAE and Precision and comparable Recall and F-measure. We also report an increase of quality in the context information presence.
08 - 25 Jan - Divide and Conquer
This document discusses divide and conquer algorithms for multiplying integers and finding the kth smallest element. It describes how to multiply integers by dividing them into left and right halves, combining results. Finding the kth smallest element involves choosing a pivot, partitioning around it, and recursively searching halves until the kth element is found. Both algorithms take O(n log n) time by dividing the problem size by a constant factor at each step.
module1
The document contains code for a VBA macro that copies rows of data from sheet1 to sheet2. It defines variables to store values from 10 columns in sheet1. It then uses nested For loops to iterate through rows 1 to 10000 in sheet1, copy the values from each row to sheet2, and increment a reference line each time to paste to the next row.
Gan
This document provides an introduction to generative adversarial networks (GANs). It includes examples of GAN applications, an overview of how GANs work with generators and discriminators, the loss functions used in GAN training, and the global optimality conditions for GAN training. It explains that the global minimum is achieved when the generated distribution pz matches the real data distribution pdata, resulting in a value of -log4 for the loss function.
Tower of hanoi
The document discusses the Tower of Hanoi problem. It contains the following key points:
1) The Tower of Hanoi problem involves moving disks of different sizes between three towers, where the rules are that larger disks cannot be placed on top of smaller disks and only one disk can be moved at a time.
2) An algorithm for solving the Tower of Hanoi problem recursively moves all but one disk from the starting tower to an auxiliary tower, then moves the remaining disk to the destination tower, and finally moves the disks from the auxiliary tower to the destination tower.
3) The number of moves required to solve a Tower of Hanoi problem increases exponentially based on the number of disks,
Tower of hanoi
The document describes the Tower of Hanoi puzzle. It involves three pegs with disks of decreasing diameters stacked on one peg. The objective is to move the entire stack to another peg following two rules: only the top disk can be moved at a time, and a larger disk can never be placed on top of a smaller disk. The algorithm works recursively by breaking the problem down into moving all disks except the largest, moving the largest disk, and then moving the remaining disks.
k*NN2016
This document discusses k*-nearest neighbors (k*-NN), an improvement on the standard k-nearest neighbors (k-NN) algorithm. k*-NN aims to find an optimal value of k for k-NN by minimizing a loss function that trades off bias and variance. The k*-NN algorithm is presented, which calculates the optimal k and sample weights. Experimental results on several datasets show that k*-NN often outperforms standard k-NN and Nadaraya-Watson kernel regression, finding a better value of k through cross-validation.
Mate tarea - 2º
The document contains examples of algebraic expressions that can be factorized. Specifically, it lists 14 different expressions involving variables like x, y, z, a, b, c, m, n, and coefficients. The expressions include differences of terms, sums of terms, products of terms with common factors that can be pulled out, and expressions within parentheses that can be distributed and combined.
[DL輪読会]近年のエネルギーベースモデルの進展
1. The document discusses energy-based models (EBMs) and how they can be applied to classifiers. It introduces noise contrastive estimation and flow contrastive estimation as methods to train EBMs.
2. One paper presented trains energy-based models using flow contrastive estimation by passing data through a flow-based generator. This allows implicit modeling with EBMs.
3. Another paper argues that classifiers can be viewed as joint energy-based models over inputs and outputs, and should be treated as such. It introduces a method to train classifiers as EBMs using contrastive divergence.
Data Structure and Algorithms The Tower of Hanoi
The document describes the Tower of Hanoi puzzle, which involves moving disks of different sizes between three pegs according to rules of only moving one disk at a time and never placing a larger disk on top of a smaller one. It provides an algorithm and recursive solution for solving the puzzle by moving disks from the source peg to the auxiliary peg and then to the destination peg. The number of minimum moves needed to solve the puzzle for n disks is 2^n - 1. For example, 4 disks requires 15 moves.
19 bcs2241 daa_3.3
This document contains code and algorithms to find all occurrences of a pattern string P in a given text string S. It includes pseudocode that uses the Knuth-Morris-Pratt algorithm to compute a prefix function to match the pattern by skipping characters in the text. The code implements this algorithm to search for a pattern in a text and print the index of any matches found.
PHP Cheat Sheet
This PHP cheat sheet summarizes the most commonly-used language features. It is ideal for those new to learning this language.
What's hot (20)
Quantum Machine Learning and QEM for Gaussian mixture models (Alessandro Luongo)
Quantum Machine Learning and QEM for Gaussian mixture models (Alessandro Luongo)
Chris hill rps_postgis_threeoutoffouraintbad_20150505_1
Chris hill rps_postgis_threeoutoffouraintbad_20150505_1
ggplot2: An Extensible Platform for Publication-quality Graphics
ggplot2: An Extensible Platform for Publication-quality Graphics
Context-Aware Recommender System Based on Boolean Matrix Factorisation
Context-Aware Recommender System Based on Boolean Matrix Factorisation
Viewers also liked
A neural attention model for sentence summarization
A neural attention model for sentence summarization, EMNLP'15 の論文紹介．http://aclweb.org/anthology/D15-1044
Brief survey of data-to-text systems
Data-to-Textシステムに関する簡単なSurvey．特に，Data-to-Textの構成要素である，Signal AnalysisとSurface Realizationにフォーカスしています．
Incorporating copying mechanism in sequene to sequence learning
This short document does not contain any meaningful information in the text. It consists of repetitive symbols and characters that do not form words or sentences. There is no discernible topic, facts, or narrative that could be summarized from the given text.
CTSUM: extracting more certain summaries for news articles
CTSUM: extracting more certain summaries for news articles, SIGIR'14 の論文紹介．http://dl.acm.org/citation.cfm?id=2609559
Neural text generation from structured data with application to the biography...
Neural text generation from structured data with application to the biography domain, EMNLP 2016 の論文紹介です。
一部の図は論文から引用しています。
論文リンク：https://www.aclweb.org/anthology/D/D16/D16-1128.pdf
Sentence compression by deletion with LSTMs
This document appears to contain mathematical functions and code but provides no context or explanation. It does not contain enough information to generate a meaningful 3 sentence summary.
Unsupervised morphology induction using word embeddings
Unsupervised morphology induction using word embeddingsの論文紹介資料．
http://www.aclweb.org/anthology/N15-1186, NAACL 2015, best paper
Learning to explain entity relationships in knowledge graphs
ACL2015読み会@すずかけ台発表資料．Learning to explain entity relationships in knowledge graphsを紹介．http://www.aclweb.org/anthology/P15-1055
Viewers also liked (8)
A neural attention model for sentence summarization
A neural attention model for sentence summarization
Incorporating copying mechanism in sequene to sequence learning
Incorporating copying mechanism in sequene to sequence learning
CTSUM: extracting more certain summaries for news articles
CTSUM: extracting more certain summaries for news articles
Neural text generation from structured data with application to the biography...
Neural text generation from structured data with application to the biography...
Unsupervised morphology induction using word embeddings
Unsupervised morphology induction using word embeddings
Learning to explain entity relationships in knowledge graphs
Learning to explain entity relationships in knowledge graphs
Recently uploaded
Building Production Ready Search Pipelines with Spark and Milvus
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
Essentials of Automations: The Art of Triggers and Actions in FME
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Pushing the limits of ePRTC: 100ns holdover for 100 days
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Full-RAG: A modern architecture for hyper-personalization
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
Recently uploaded (20)
Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
Full-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalization
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
Beyond clicks dwell time for personalization
- 14. (t3 t2) + (t5 t4)
- 15. i Commentt1 t2 t3 t4 t5 (t2 t1) + (t5 t4) (t4 t1) i Click j k niClick Click Click i
- 23. xq,d xq,d = g(q, d) l yq,d
- 26. yd,u 2
- 28. f(x) ft(x) = Tt(x; ⇥) + TX t=1 tTt(x; ⇥t) L(yi, fT (xi)) Tt(x; ⇥t) ˆ⇥ = arg min NX i wi( Git tTt(xi; ⇥)) t = arg min NX i L(yi, ft 1(xi) + T(xi, ✓))
- 30. DCGk = kX i=1 2reli 1 log2(i + 1) NDCGk = DCGk IDCGk
- 31. MAP = PU u=1 AveP(u) U AveP(u) = PN k=1(P(k) ⇥ rel(k)) # of relevant articles for user u
- 35. I = 0 B B B B B B @ r11 r12 ... r1j ... r1N r21 r22 ... r2j ... r2N ... ... ri1 ri2 ... rij ... riN ... ... rM1 rM2 ... rMj ... rMN 1 C C C C C C A arg min U,V MX i=1 X ri,j <ri,k Ui(Vj Vk)T + (|U|2 + |V |2) I = U ⇥ V T ˆrij = Ui ⇥ V T j 2 2