Ying Wen
Shanghai Jiao Tong University
67 Papers
353 Citations
Ying Wen is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Computer science & Reinforcement learning. The author has an hindex of 11, co-authored 42 publications. Previous affiliations of Ying Wen include University College London & Peking University.
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Papers
Product-Based Neural Networks for User Response Prediction
TL;DR: A Product-based Neural Networks (PNN) with an embedding layer to learn a distributed representation of the categorical data, a product layer to capture interactive patterns between interfield categories, and further fully connected layers to explore high-order feature interactions.
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Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games
TL;DR: This paper introduces a Multiagent Bidirectionally-Coordinated Network (BiCNet) with a vectorised extension of actor-critic formulation and demonstrates that without any supervisions such as human demonstrations or labelled data, BiCNet could learn various types of advanced coordination strategies that have been commonly used by experienced game players.
391
SCConv: Spatial and Channel Reconstruction Convolution for Feature Redundancy
Jiafeng Li,Ying Wen,Lianghua He +2 more
- 01 Jun 2023
TL;DR: An attempt to exploit spatial and channel redundancy among features for CNN compression and propose an efficient convolution module, called SCConv (Spatial and Channel reconstruction Convolution), to decrease redundant computing and facilitate representative feature learning.
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Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games.
Peng Peng,Quan Yuan,Ying Wen,Yaodong Yang,Zhenkun Tang,Haitao Long,Jun Wang +6 more
- 29 Mar 2017
TL;DR: This analysis demonstrates that without any supervisions such as human demonstrations or labelled data, BiCNet could learn various types of coordination strategies that is similar to these of experienced game players, and is easily adaptable to the tasks with heterogeneous agents.
238
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Product-based Neural Networks for User Response Prediction
TL;DR: Product-based Neural Networks (PNN) as mentioned in this paper uses an embedding layer to learn a distributed representation of the categorical data, a product layer to capture interactive patterns between inter-field categories and further fully connected layers to explore high-order feature interactions.
238