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ImageNet: A large-scale hierarchical image database
TLDR
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. Expand
GloVe: Global Vectors for Word Representation
TLDR
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. Expand
ImageNet: A large-scale hierarchical image database
The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data.Expand
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
TLDR
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. Expand
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
TLDR
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. Expand
Reasoning With Neural Tensor Networks for Knowledge Base Completion
TLDR
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. Expand
Pointer Sentinel Mixture Models
TLDR
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. Expand
Regularizing and Optimizing LSTM Language Models
TLDR
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. Expand
A Deep Reinforced Model for Abstractive Summarization
TLDR
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. Expand
Improving Word Representations via Global Context and Multiple Word Prototypes
TLDR
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. Expand
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