• Publications
  • Influence
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
TLDR
It is found that embeddings learned from the bilinear objective are particularly good at capturing relational semantics and that the composition of relations is characterized by matrix multiplication. Expand
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
TLDR
This article provides an overview of progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition. Expand
Learning deep structured semantic models for web search using clickthrough data
TLDR
A series of new latent semantic models with a deep structure that project queries and documents into a common low-dimensional space where the relevance of a document given a query is readily computed as the distance between them are developed. Expand
Deep Neural Networks for Acoustic Modeling in Speech Recognition
TLDR
This paper provides an overview of this progress and repres nts the shared views of four research groups who have had recent successes in using deep neural networks for a coustic modeling in speech recognition. Expand
Stacked Attention Networks for Image Question Answering
TLDR
A multiple-layer SAN is developed in which an image is queried multiple times to infer the answer progressively, and the progress that the SAN locates the relevant visual clues that lead to the answer of the question layer-by-layer. Expand
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
TLDR
This new dataset is aimed to overcome a number of well-known weaknesses of previous publicly available datasets for the same task of reading comprehension and question answering, and is the most comprehensive real-world dataset of its kind in both quantity and quality. Expand
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition
TLDR
A pre-trained deep neural network hidden Markov model (DNN-HMM) hybrid architecture that trains the DNN to produce a distribution over senones (tied triphone states) as its output that can significantly outperform the conventional context-dependent Gaussian mixture model (GMM)-HMMs. Expand
Deep Learning: Methods and Applications
  • L. Deng, Dong Yu
  • Computer Science
  • Found. Trends Signal Process.
  • 12 June 2014
TLDR
This monograph provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks, including natural language and text processing, information retrieval, and multimodal information processing empowered by multi-task deep learning. Expand
From captions to visual concepts and back
TLDR
This paper uses multiple instance learning to train visual detectors for words that commonly occur in captions, including many different parts of speech such as nouns, verbs, and adjectives, and develops a maximum-entropy language model. Expand
A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval
TLDR
A new latent semantic model that incorporates a convolutional-pooling structure over word sequences to learn low-dimensional, semantic vector representations for search queries and Web documents is proposed. Expand
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