This document discusses neural word embeddings and how they represent words as dense vectors in a continuous vector space to capture semantic and syntactic relationships between words. It describes how word embeddings learn regularities through neural network language models like the skip-gram model, with techniques like negative sampling and hierarchical softmax. Word embeddings can also learn phrases and model their compositionality through additive combinations of word vectors.
This document discusses neural word embeddings and how they represent words as dense vectors in a continuous vector space to capture semantic and syntactic relationships between words. It describes how word embeddings learn regularities through neural network language models like the skip-gram model, with techniques like negative sampling and hierarchical softmax. Word embeddings can also learn phrases and model their compositionality through additive combinations of word vectors.
This document discusses Recurrent Neural Networks (RNNs) and provides information about different types of RNNs including vanilla RNNs, LSTM RNNs, and GRU RNNs. It covers topics such as backpropagation through time, exploding and vanishing gradients, and the equations that define LSTM and GRU units. The document is a workshop on RNNs presented by Intelligent City Ltd. and their CEO Shindong Kang.
This document discusses various applications of neural networks, including pattern recognition, autonomous vehicles, medicine, sports prediction, and virus detection. Some key applications mentioned are using neural networks for patient diagnosis, detecting coronary artery disease from medical images, predicting sports outcomes based on team statistics, and forecasting space weather events. The document also notes some limitations of neural networks, such as requiring large datasets and not providing explanations for decisions.
Recurrent Neural Networks. Part 1: TheoryAndrii Gakhov
The document provides an overview of recurrent neural networks (RNNs) and their advantages over feedforward neural networks. It describes the basic structure and training of RNNs using backpropagation through time. RNNs can process sequential data of variable lengths, unlike feedforward networks. However, RNNs are difficult to train due to vanishing and exploding gradients. More advanced RNN architectures like LSTMs and GRUs address this by introducing gating mechanisms that allow the network to better control the flow of information.
This document contains study of Peer to Peer Distributed system.Three Models of Distributed system.Such as Centralizes,Decentralized,Hybird Model and Pros and cons of these models. Skpye and Bit torrent architecture is also discussed.This tutorial can be very help full for those who are beginners.
Exploring Session Context using Distributed Representations of Queries and Re...Bhaskar Mitra
Search logs contain examples of frequently occurring patterns of user reformulations of queries. Intuitively, the reformulation "san francisco" → "san francisco 49ers" is semantically similar to "detroit" →"detroit lions". Likewise, "london"→"things to do in london" and "new york"→"new york tourist attractions" can also be considered similar transitions in intent. The reformulation "movies" → "new movies" and "york" → "new york", however, are clearly different despite the lexical similarities in the two reformulations. In this paper, we study the distributed representation of queries learnt by deep neural network models, such as the Convolutional Latent Semantic Model, and show that they can be used to represent query reformulations as vectors. These reformulation vectors exhibit favourable properties such as mapping semantically and syntactically similar query changes closer in the embedding space. Our work is motivated by the success of continuous space language models in capturing relationships between words and their meanings using offset vectors. We demonstrate a way to extend the same intuition to represent query reformulations.
Furthermore, we show that the distributed representations of queries and reformulations are both useful for modelling session context for query prediction tasks, such as for query auto-completion (QAC) ranking. Our empirical study demonstrates that short-term (session) history context features based on these two representations improves the mean reciprocal rank (MRR) for the QAC ranking task by more than 10% over a supervised ranker baseline. Our results also show that by using features based on both these representations together we achieve a better performance, than either of them individually.
Paper: http://research.microsoft.com/apps/pubs/default.aspx?id=244728
This document discusses various methods of knowledge representation in artificial intelligence, including semantic networks, conceptual graphs, frames, and scripts. It provides examples of each method through figures and descriptions. Different knowledge representation schemes are compared, along with their suitability for representing different types of knowledge.
Peer to peer (P2P) computing involves direct sharing of resources and services between systems without centralized control or servers. P2P systems can be either pure, with no central server and peers communicating directly, or hybrid with a centralized server for name resolution but direct peer-to-peer communication. P2P is commonly used for applications that involve parallelizable or componentized tasks, content/file sharing, and collaboration where users can interact and edit shared information.
Knowledge Representation in Artificial intelligence Yasir Khan
This document discusses different methods of knowledge representation in artificial intelligence, including logical representations, semantic networks, production rules, and frames. Logical representations use formal logics like propositional logic and first-order predicate logic to represent facts and relationships. Semantic networks represent knowledge graphically as nodes and edges to model concepts and their relationships. Production rules represent knowledge as condition-action pairs to model problem-solving. Frames represent stereotyped situations as templates with slots to model attributes and behaviors. Choosing the right knowledge representation method is important for building successful AI systems.
This document provides an introduction to neural networks, including their basic components and types. It discusses neurons, activation functions, different types of neural networks based on connection type, topology, and learning methods. It also covers applications of neural networks in areas like pattern recognition and control systems. Neural networks have advantages like the ability to learn from experience and handle incomplete information, but also disadvantages like the need for training and high processing times for large networks. In conclusion, neural networks can provide more human-like artificial intelligence by taking approximation and hard-coded reactions out of AI design, though they still require fine-tuning.
Peer-to-peer (P2P) networks are a type of computer network architecture where individuals form a loose group to share resources directly with others in the group without a centralized server. There are two main types of P2P network structures - unstructured and structured. Unstructured networks do not use algorithms to organize the network, while structured networks use algorithms to optimize routing. Popular applications of P2P networking include file sharing, media streaming, grid computing, instant messaging, and voice over internet protocol.
This document discusses Recurrent Neural Networks (RNNs) and provides information about different types of RNNs including vanilla RNNs, LSTM RNNs, and GRU RNNs. It covers topics such as backpropagation through time, exploding and vanishing gradients, and the equations that define LSTM and GRU units. The document is a workshop on RNNs presented by Intelligent City Ltd. and their CEO Shindong Kang.
This document discusses various applications of neural networks, including pattern recognition, autonomous vehicles, medicine, sports prediction, and virus detection. Some key applications mentioned are using neural networks for patient diagnosis, detecting coronary artery disease from medical images, predicting sports outcomes based on team statistics, and forecasting space weather events. The document also notes some limitations of neural networks, such as requiring large datasets and not providing explanations for decisions.
Recurrent Neural Networks. Part 1: TheoryAndrii Gakhov
The document provides an overview of recurrent neural networks (RNNs) and their advantages over feedforward neural networks. It describes the basic structure and training of RNNs using backpropagation through time. RNNs can process sequential data of variable lengths, unlike feedforward networks. However, RNNs are difficult to train due to vanishing and exploding gradients. More advanced RNN architectures like LSTMs and GRUs address this by introducing gating mechanisms that allow the network to better control the flow of information.
This document contains study of Peer to Peer Distributed system.Three Models of Distributed system.Such as Centralizes,Decentralized,Hybird Model and Pros and cons of these models. Skpye and Bit torrent architecture is also discussed.This tutorial can be very help full for those who are beginners.
Exploring Session Context using Distributed Representations of Queries and Re...Bhaskar Mitra
Search logs contain examples of frequently occurring patterns of user reformulations of queries. Intuitively, the reformulation "san francisco" → "san francisco 49ers" is semantically similar to "detroit" →"detroit lions". Likewise, "london"→"things to do in london" and "new york"→"new york tourist attractions" can also be considered similar transitions in intent. The reformulation "movies" → "new movies" and "york" → "new york", however, are clearly different despite the lexical similarities in the two reformulations. In this paper, we study the distributed representation of queries learnt by deep neural network models, such as the Convolutional Latent Semantic Model, and show that they can be used to represent query reformulations as vectors. These reformulation vectors exhibit favourable properties such as mapping semantically and syntactically similar query changes closer in the embedding space. Our work is motivated by the success of continuous space language models in capturing relationships between words and their meanings using offset vectors. We demonstrate a way to extend the same intuition to represent query reformulations.
Furthermore, we show that the distributed representations of queries and reformulations are both useful for modelling session context for query prediction tasks, such as for query auto-completion (QAC) ranking. Our empirical study demonstrates that short-term (session) history context features based on these two representations improves the mean reciprocal rank (MRR) for the QAC ranking task by more than 10% over a supervised ranker baseline. Our results also show that by using features based on both these representations together we achieve a better performance, than either of them individually.
Paper: http://research.microsoft.com/apps/pubs/default.aspx?id=244728
This document discusses various methods of knowledge representation in artificial intelligence, including semantic networks, conceptual graphs, frames, and scripts. It provides examples of each method through figures and descriptions. Different knowledge representation schemes are compared, along with their suitability for representing different types of knowledge.
Peer to peer (P2P) computing involves direct sharing of resources and services between systems without centralized control or servers. P2P systems can be either pure, with no central server and peers communicating directly, or hybrid with a centralized server for name resolution but direct peer-to-peer communication. P2P is commonly used for applications that involve parallelizable or componentized tasks, content/file sharing, and collaboration where users can interact and edit shared information.
Knowledge Representation in Artificial intelligence Yasir Khan
This document discusses different methods of knowledge representation in artificial intelligence, including logical representations, semantic networks, production rules, and frames. Logical representations use formal logics like propositional logic and first-order predicate logic to represent facts and relationships. Semantic networks represent knowledge graphically as nodes and edges to model concepts and their relationships. Production rules represent knowledge as condition-action pairs to model problem-solving. Frames represent stereotyped situations as templates with slots to model attributes and behaviors. Choosing the right knowledge representation method is important for building successful AI systems.
This document provides an introduction to neural networks, including their basic components and types. It discusses neurons, activation functions, different types of neural networks based on connection type, topology, and learning methods. It also covers applications of neural networks in areas like pattern recognition and control systems. Neural networks have advantages like the ability to learn from experience and handle incomplete information, but also disadvantages like the need for training and high processing times for large networks. In conclusion, neural networks can provide more human-like artificial intelligence by taking approximation and hard-coded reactions out of AI design, though they still require fine-tuning.
Peer-to-peer (P2P) networks are a type of computer network architecture where individuals form a loose group to share resources directly with others in the group without a centralized server. There are two main types of P2P network structures - unstructured and structured. Unstructured networks do not use algorithms to organize the network, while structured networks use algorithms to optimize routing. Popular applications of P2P networking include file sharing, media streaming, grid computing, instant messaging, and voice over internet protocol.
20150701 Improving SMT quality with morpho-syntactic analysis
Distributed Representations of Words and Phrases and their Compositionally
1. Distributed Representations of Words and
Phrases and their Compositionally
長岡技術科学大学 自然言語処理研究室
高橋寛治
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013).
Distributed Representations of Words and Phrases and their
Compositionality. Advances in Neural Information Processing
Systems 26 (NIPS 2013)
「word2vecによる自然言語処理」の図を利用
文献紹介 2016年4月13日