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ImageNet: A large-scale hierarchical image database
- Jia Deng, Wei Dong, R. Socher, Li-Jia Li, K. Li, Li Fei-Fei
- Computer ScienceIEEE Conference on Computer Vision and Pattern…
- 20 June 2009
A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
GloVe: Global Vectors for Word Representation
A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
A Sentiment Treebank that includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality, and introduces the Recursive Neural Tensor Network.
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
The Tree-LSTM is introduced, a generalization of LSTMs to tree-structured network topologies that outperform all existing systems and strong LSTM baselines on two tasks: predicting the semantic relatedness of two sentences and sentiment classification.
Reasoning With Neural Tensor Networks for Knowledge Base Completion
An expressive neural tensor network suitable for reasoning over relationships between two entities given a subset of the knowledge base is introduced and performance can be improved when entities are represented as an average of their constituting word vectors.
Pointer Sentinel Mixture Models
The pointer sentinel-LSTM model achieves state of the art language modeling performance on the Penn Treebank while using far fewer parameters than a standard softmax LSTM and the freely available WikiText corpus is introduced.
Regularizing and Optimizing LSTM Language Models
This paper proposes the weight-dropped LSTM which uses DropConnect on hidden-to-hidden weights as a form of recurrent regularization and introduces NT-ASGD, a variant of the averaged stochastic gradient method, wherein the averaging trigger is determined using a non-monotonic condition as opposed to being tuned by the user.
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
This work proposes Seq2 SQL, a deep neural network for translating natural language questions to corresponding SQL queries, and releases WikiSQL, a dataset of 80654 hand-annotated examples of questions and SQL queries distributed across 24241 tables fromWikipedia that is an order of magnitude larger than comparable datasets.
A Deep Reinforced Model for Abstractive Summarization
A neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL) that produces higher quality summaries.
Improving Word Representations via Global Context and Multiple Word Prototypes
A new neural network architecture is presented which learns word embeddings that better capture the semantics of words by incorporating both local and global document context, and accounts for homonymy and polysemy by learning multiple embedDings per word.