J
Jiang Bian
Researcher at Microsoft
Publications - 191
Citations - 4966
Jiang Bian is an academic researcher from Microsoft. The author has contributed to research in topics: Computer science & Chemistry. The author has an hindex of 27, co-authored 108 publications receiving 3392 citations. Previous affiliations of Jiang Bian include Georgia Institute of Technology & Yahoo!.
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Journal ArticleDOI
Deep Subdomain Adaptation Network for Image Classification
TL;DR: This work presents a deep subdomain adaptation network (DSAN) that learns a transfer network by aligning the relevant subdomain distributions of domain-specific layer activations across different domains based on a local maximum mean discrepancy (LMMD).
Proceedings ArticleDOI
Finding the right facts in the crowd: factoid question answering over social media
TL;DR: A general ranking framework for factual information retrieval from social media is presented and results of a large scale evaluation demonstrate that the method is highly effective at retrieving well-formed, factual answers to questions, as evaluated on a standard factoid QA benchmark.
Posted Content
Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks
TL;DR: Wang et al. as discussed by the authors introduced a novel framework based on Recurrent Neural Networks (RNN), which directly models the dependency on user's sequential behaviors into the click prediction process through the recurrent structure in RNN.
Proceedings ArticleDOI
Predicting information seeker satisfaction in community question answering
TL;DR: This paper attempts to predict whether a question author will be satisfied with the answers submitted by the community participants, and presents a general prediction model, and develops a variety of content, structure, and community-focused features for this task.
Proceedings ArticleDOI
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction
TL;DR: Wang et al. as mentioned in this paper designed a Hybrid Attention Networks (HAN) to predict the stock trend based on the sequence of recent related news, and applied the self-paced learning mechanism to imitate the third principle.