3 citations · 4 across the 5 of their papers we have counts for
5 papers
Deeply Virtual Compton Scattering at Future Electron-Ion Colliders
Gang Xie, Wei Kou, Qiang Fu +2
The study of hadronic structure has been carried out for many years. Generalized parton distribution functions (GPDs) give broad information on the internal structure of hadrons. C…
Does Deep Learning Learn to Abstract? A Systematic Probing Framework
Shengnan An, Zeqi Lin, Bei Chen +3
Abstraction is a desirable capability for deep learning models, which means to induce abstract concepts from concrete instances and flexibly apply them beyond the learning context.…
Revisiting Estimation Bias in Policy Gradients for Deep Reinforcement Learning
Haoxuan Pan, Deheng Ye, Xiaoming Duan +4
We revisit the estimation bias in policy gradients for the discounted episodic Markov decision process (MDP) from Deep Reinforcement Learning (DRL) perspective. The objective is fo…
Sample Dropout: A Simple yet Effective Variance Reduction Technique in Deep Policy Optimization
Zichuan Lin, Xiapeng Wu, Mingfei Sun +4
Recent success in Deep Reinforcement Learning (DRL) methods has shown that policy optimization with respect to an off-policy distribution via importance sampling is effective for s…
Personalized Acoustic Echo Cancellation for Full-duplex Communications
Shimin Zhang, Ziteng Wang, Yukai Ju +4
Deep neural networks (DNNs) have shown promising results for acoustic echo cancellation (AEC). But the DNN-based AEC models let through all near-end speakers including the interfer…