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Benyou Wang
Researcher at University of Padua
Publications - 66
Citations - 1770
Benyou Wang is an academic researcher from University of Padua. The author has contributed to research in topics: Computer science & Question answering. The author has an hindex of 17, co-authored 39 publications receiving 1207 citations. Previous affiliations of Benyou Wang include Tianjin University & Tencent.
Papers
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Proceedings ArticleDOI
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
TL;DR: A unified framework takes advantage of both schools of thinking in information retrieval modelling and shows that the generative model learns to fit the relevance distribution over documents via the signals from the discriminative model to achieve a better estimation for document ranking.
Proceedings ArticleDOI
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
TL;DR: In this paper, a game theoretical minimax game is proposed to iteratively optimise both generative and discriminative models for document ranking, and the generative model is trained to fit the relevance distribution over documents via the signals from the discriminator.
Journal ArticleDOI
Detection of subtype blood cells using deep learning
Prayag Tiwari,Jia Qian,Qiuchi Li,Benyou Wang,Deepak Gupta,Ashish Khanna,Joel J. P. C. Rodrigues,Victor Hugo C. de Albuquerque +7 more
TL;DR: A CNN-based framework is built to automatically classify the blood cell images into subtypes of the cells, and the results show that the proposed model provide better results in terms of evaluation parameters.
Proceedings Article
End-to-End Quantum-like Language Models with Application to Question Answering
TL;DR: A Neural Network based Quantum-like Language Model (NNQLM) is developed and applied to Question Answering, which represents a sentence and encodes a mixture of semantic subspaces and can be integrated into neural network architectures.
Proceedings Article
Encoding word order in complex embeddings
TL;DR: This work presents a novel and principled solution for modeling both the global absolute positions of words and their order relationships, and is the first work in NLP to link imaginary numbers in complex-valued representations to concrete meanings (i.e., word order).