J
Jianfeng Gao
Researcher at Microsoft
Publications - 604
Citations - 54964
Jianfeng Gao is an academic researcher from Microsoft. The author has contributed to research in topics: Computer science & Language model. The author has an hindex of 94, co-authored 505 publications receiving 40075 citations. Previous affiliations of Jianfeng Gao include Duke University & Carnegie Mellon University.
Papers
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Proceedings Article
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
TL;DR: 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.
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Stacked Attention Networks for Image Question Answering
TL;DR: In this paper, a stacked attention network (SAN) is proposed to learn to answer natural language questions from images by using semantic representation of a question as query to search for the regions in an image that are related to the answer.
Proceedings ArticleDOI
Learning deep structured semantic models for web search using clickthrough data
TL;DR: 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.
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A Diversity-Promoting Objective Function for Neural Conversation Models
TL;DR: The authors proposed using Maximum Mutual Information (MMI) as the objective function in neural models to generate more diverse, interesting, and appropriate responses, yielding substantive gains in BLEU scores on two conversational datasets.
Proceedings ArticleDOI
From captions to visual concepts and back
Hao Fang,Saurabh Gupta,Forrest Iandola,Rupesh Kumar Srivastava,Li Deng,Piotr Dollár,Jianfeng Gao,Xiaodong He,Margaret Mitchell,John Platt,C. Lawrence Zitnick,Geoffrey Zweig +11 more
TL;DR: This paper used 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, which serve as conditional inputs to a maximum-entropy language model.