Search Results for author: Qi Zhou

Found 7 papers, 2 papers with code

EvaLDA: Efficient Evasion Attacks Towards Latent Dirichlet Allocation

1 code implementation9 Dec 2020 Qi Zhou, Haipeng Chen, Yitao Zheng, Zhen Wang

As one of the most powerful topic models, Latent Dirichlet Allocation (LDA) has been used in a vast range of tasks, including document understanding, information retrieval and peer-reviewer assignment.

Information Retrieval Sentiment Analysis +1

Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method

no code implementations NeurIPS 2020 Qi Zhou, Yufei Kuang, Zherui Qiu, Houqiang Li, Jie Wang

However, in continuous action spaces, integrating entropy regularization with expressive policies is challenging and usually requires complex inference procedures.

Continuous Control

Intelligent Bandwidth Allocation for Latency Management in NG-EPON using Reinforcement Learning Methods

no code implementations21 Jan 2020 Qi Zhou, Jingjie Zhu, Junwen Zhang, Zhensheng Jia, Bernardo Huberman, Gee-Kung Chang

A novel intelligent bandwidth allocation scheme in NG-EPON using reinforcement learning is proposed and demonstrated for latency management.

Evidence for Bosonization in a three-dimensional gas of SU($N$) fermions

no code implementations27 Dec 2019 Bo Song, Yangqian Yan, Chengdong He, Zejian Ren, Qi Zhou, Gyu-Boong Jo

Blurring the boundary between bosons and fermions lies at the heart of a wide range of intriguing quantum phenomena in multiple disciplines, ranging from condensed matter physics and atomic, molecular and optical physics to high energy physics.

Quantum Gases Other Condensed Matter Quantum Physics

Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization

1 code implementation28 Nov 2019 Qi Zhou, Houqiang Li, Jie Wang

In this paper, We propose a Policy Optimization method with Model-Based Uncertainty (POMBU)---a novel model-based approach---that can effectively improve the asymptotic performance using the uncertainty in Q-values.

Model-based Reinforcement Learning

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