Search Results for author: Di You

Found 6 papers, 4 papers with code

COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive Sensing

1 code implementation15 Jul 2021 Di You, Jian Zhang, Jingfen Xie, Bin Chen, Siwei Ma

In this paper, we propose a novel COntrollable Arbitrary-Sampling neTwork, dubbed COAST, to solve CS problems of arbitrary-sampling matrices (including unseen sampling matrices) with one single model.

Compressive Sensing

ISTA-Net++: Flexible Deep Unfolding Network for Compressive Sensing

1 code implementation22 Mar 2021 Di You, Jingfen Xie, Jian Zhang

While deep neural networks have achieved impressive success in image compressive sensing (CS), most of them lack flexibility when dealing with multi-ratio tasks and multi-scene images in practical applications.

Compressive Sensing

Quaternion-Based Self-Attentive Long Short-Term User Preference Encoding for Recommendation

no code implementations31 Aug 2020 Thanh Tran, Di You, Kyumin Lee

Quaternion space has brought several benefits over the traditional Euclidean space: Quaternions (i) consist of a real and three imaginary components, encouraging richer representations; (ii) utilize Hamilton product which better encodes the inter-latent interactions across multiple Quaternion components; and (iii) result in a model with smaller degrees of freedom and less prone to overfitting.

Recommendation Systems

Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation

1 code implementation7 Jan 2020 Di You, Nguyen Vo, Kyumin Lee, Qiang Liu

To combat fake news, researchers mostly focused on detecting fake news and journalists built and maintained fact-checking sites (e. g., Snopes. com and Politifact. com).

Fact Checking Graph Attention +1

Bridging the Gap between Training and Inference for Neural Machine Translation

no code implementations ACL 2019 Wen Zhang, Yang Feng, Fandong Meng, Di You, Qun Liu

Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words.

Machine Translation Translation

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