Search Results for author: Li Zhao

Found 33 papers, 9 papers with code

Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition

no code implementations17 Feb 2023 Yan Zhao, Jincen Wang, Yuan Zong, Wenming Zheng, Hailun Lian, Li Zhao

In this paper, we propose a novel deep transfer learning method called deep implicit distribution alignment networks (DIDAN) to deal with cross-corpus speech emotion recognition (SER) problem, in which the labeled training (source) and unlabeled testing (target) speech signals come from different corpora.

Cross-corpus Speech Emotion Recognition +1

An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context

no code implementations24 Dec 2022 Xiaoyu Chen, Xiangming Zhu, Yufeng Zheng, Pushi Zhang, Li Zhao, Wenxue Cheng, Peng Cheng, Yongqiang Xiong, Tao Qin, Jianyu Chen, Tie-Yan Liu

One of the key challenges in deploying RL to real-world applications is to adapt to variations of unknown environment contexts, such as changing terrains in robotic tasks and fluctuated bandwidth in congestion control.

Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management

no code implementations15 Dec 2022 Yuandong Ding, Mingxiao Feng, Guozi Liu, Wei Jiang, Chuheng Zhang, Li Zhao, Lei Song, Houqiang Li, Yan Jin, Jiang Bian

In this paper, we consider the inventory management (IM) problem where we need to make replenishment decisions for a large number of stock keeping units (SKUs) to balance their supply and demand.

Management Multi-agent Reinforcement Learning +2

TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets

1 code implementation5 Dec 2022 Yuanying Cai, Chuheng Zhang, Li Zhao, Wei Shen, Xuyun Zhang, Lei Song, Jiang Bian, Tao Qin, TieYan Liu

There are two challenges for this setting: 1) The optimal trade-off between optimizing the RL signal and the behavior cloning (BC) signal changes on different states due to the variation of the action coverage induced by different behavior policies.

D4RL Offline RL +2

Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation

no code implementations18 Jul 2022 Guoqing Liu, Mengzhang Cai, Li Zhao, Tao Qin, Adrian Brown, Jimmy Bischoff, Tie-Yan Liu

In this work, we propose using only screenshots/pixels as input for automated game testing and build a general game testing agent, Inspector, that can be easily applied to different games without deep integration with games.

Imitation Learning

Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret

1 code implementation25 May 2022 Jiawei Huang, Li Zhao, Tao Qin, Wei Chen, Nan Jiang, Tie-Yan Liu

We propose a new learning framework that captures the tiered structure of many real-world user-interaction applications, where the users can be divided into two groups based on their different tolerance on exploration risks and should be treated separately.

reinforcement-learning Reinforcement Learning (RL)

Curriculum Offline Imitating Learning

no code implementations NeurIPS 2021 Minghuan Liu, Hanye Zhao, Zhengyu Yang, Jian Shen, Weinan Zhang, Li Zhao, Tie-Yan Liu

However, IL is usually limited in the capability of the behavioral policy and tends to learn a mediocre behavior from the dataset collected by the mixture of policies.

Continuous Control Imitation Learning +2

Curriculum Offline Imitation Learning

1 code implementation3 Nov 2021 Minghuan Liu, Hanye Zhao, Zhengyu Yang, Jian Shen, Weinan Zhang, Li Zhao, Tie-Yan Liu

However, IL is usually limited in the capability of the behavioral policy and tends to learn a mediocre behavior from the dataset collected by the mixture of policies.

Continuous Control Imitation Learning +2

Distributional Reinforcement Learning for Multi-Dimensional Reward Functions

no code implementations NeurIPS 2021 Pushi Zhang, Xiaoyu Chen, Li Zhao, Wei Xiong, Tao Qin, Tie-Yan Liu

To fully inherit the benefits of distributional RL and hybrid reward architectures, we introduce Multi-Dimensional Distributional DQN (MD3QN), which extends distributional RL to model the joint return distribution from multiple reward sources.

Distributional Reinforcement Learning reinforcement-learning +1

Multi-Agent Reinforcement Learning with Shared Resource in Inventory Management

no code implementations29 Sep 2021 Mingxiao Feng, Guozi Liu, Li Zhao, Lei Song, Jiang Bian, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu

We consider inventory management (IM) problem for a single store with a large number of SKUs (stock keeping units) in this paper, where we need to make replenishment decisions for each SKU to balance its supply and demand.

Management Multi-agent Reinforcement Learning +2

Concept-Based Label Embedding via Dynamic Routing for Hierarchical Text Classification

no code implementations ACL 2021 Xuepeng Wang, Li Zhao, Bing Liu, Tao Chen, Feng Zhang, Di Wang

In this paper, we propose a novel concept-based label embedding method that can explicitly represent the concept and model the sharing mechanism among classes for the hierarchical text classification.

text-classification Text Classification

Return-Based Contrastive Representation Learning for Reinforcement Learning

no code implementations ICLR 2021 Guoqing Liu, Chuheng Zhang, Li Zhao, Tao Qin, Jinhua Zhu, Jian Li, Nenghai Yu, Tie-Yan Liu

Recently, various auxiliary tasks have been proposed to accelerate representation learning and improve sample efficiency in deep reinforcement learning (RL).

Atari Games reinforcement-learning +2

Design and Commissioning of the PandaX-4T Cryogenic Distillation System for Krypton and Radon Removal

no code implementations4 Dec 2020 Xiangyi Cui, Zhou Wang, Yonglin Ju, Xiuli Wang, Huaxuan Liu, Wenbo Ma, Jianglai Liu, Li Zhao, Xiangdong Ji, Shuaijie Li, Rui Yan, Haidong Sha, Peiyao Huang

An online cryogenic distillation system for the removal of krypton and radon from xenon was designed and constructed for PandaX-4T, a highly sensitive dark matter detection experiment.

Instrumentation and Detectors High Energy Physics - Experiment

RD$^2$: Reward Decomposition with Representation Decomposition

no code implementations NeurIPS 2020 Zichuan Lin, Derek Yang, Li Zhao, Tao Qin, Guangwen Yang, Tie-Yan Liu

In this work, we propose a set of novel reward decomposition principles by constraining uniqueness and compactness of different state features/representations relevant to different sub-rewards.

A Multi-stream Convolutional Neural Network for Micro-expression Recognition Using Optical Flow and EVM

no code implementations7 Nov 2020 Jinming Liu, Ke Li, Baolin Song, Li Zhao

On the other hand, some methods based on deep learning also cannot get high accuracy due to problems such as the imbalance of databases.

Micro-Expression Recognition Optical Flow Estimation

Tensor Perturbations and Thick Branes in Higher-dimensional $f(R)$ Gravity

no code implementations1 Sep 2020 Zheng-Quan Cui, Zi-Chao Lin, Jun-Jie Wan, Yu-Xiao Liu, Li Zhao

At last, the effective potential of the Kaluza-Klein modes of the graviton is discussed for the two solved $f(R)$ models in higher dimensions.

High Energy Physics - Theory General Relativity and Quantum Cosmology

Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge

no code implementations4 Jul 2020 Yue Sun, Kun Gao, Zhengwang Wu, Zhihao Lei, Ying WEI, Jun Ma, Xiaoping Yang, Xue Feng, Li Zhao, Trung Le Phan, Jitae Shin, Tao Zhong, Yu Zhang, Lequan Yu, Caizi Li, Ramesh Basnet, M. Omair Ahmad, M. N. S. Swamy, Wenao Ma, Qi Dou, Toan Duc Bui, Camilo Bermudez Noguera, Bennett Landman, Ian H. Gotlib, Kathryn L. Humphreys, Sarah Shultz, Longchuan Li, Sijie Niu, Weili Lin, Valerie Jewells, Gang Li, Dinggang Shen, Li Wang

Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.

Brain Segmentation

Suphx: Mastering Mahjong with Deep Reinforcement Learning

no code implementations30 Mar 2020 Junjie Li, Sotetsu Koyamada, Qiwei Ye, Guoqing Liu, Chao Wang, Ruihan Yang, Li Zhao, Tao Qin, Tie-Yan Liu, Hsiao-Wuen Hon

Artificial Intelligence (AI) has achieved great success in many domains, and game AI is widely regarded as its beachhead since the dawn of AI.

reinforcement-learning Reinforcement Learning (RL)

Distributional Reward Decomposition for Reinforcement Learning

no code implementations NeurIPS 2019 Zichuan Lin, Li Zhao, Derek Yang, Tao Qin, Guangwen Yang, Tie-Yan Liu

Many reinforcement learning (RL) tasks have specific properties that can be leveraged to modify existing RL algorithms to adapt to those tasks and further improve performance, and a general class of such properties is the multiple reward channel.

reinforcement-learning Reinforcement Learning (RL)

Fully Parameterized Quantile Function for Distributional Reinforcement Learning

6 code implementations NeurIPS 2019 Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, Tie-Yan Liu

The key challenge in practical distributional RL algorithms lies in how to parameterize estimated distributions so as to better approximate the true continuous distribution.

Ranked #3 on Atari Games on Atari 2600 Skiing (using extra training data)

Atari Games Distributional Reinforcement Learning +2

Independence-aware Advantage Estimation

no code implementations25 Sep 2019 Pushi Zhang, Li Zhao, Guoqing Liu, Jiang Bian, Minglie Huang, Tao Qin, Tie-Yan Liu

Most of existing advantage function estimation methods in reinforcement learning suffer from the problem of high variance, which scales unfavorably with the time horizon.

Demonstration Actor Critic

no code implementations25 Sep 2019 Guoqing Liu, Li Zhao, Pushi Zhang, Jiang Bian, Tao Qin, Nenghai Yu, Tie-Yan Liu

One approach leverages demonstration data in a supervised manner, which is simple and direct, but can only provide supervision signal over those states seen in the demonstrations.

Reinforcement Learning for Relation Classification from Noisy Data

2 code implementations24 Aug 2018 Jun Feng, Minlie Huang, Li Zhao, Yang Yang, Xiaoyan Zhu

In this paper, we propose a novel model for relation classification at the sentence level from noisy data.

Classification reinforcement-learning +1

Efficient Sequence Learning with Group Recurrent Networks

no code implementations NAACL 2018 Fei Gao, Lijun Wu, Li Zhao, Tao Qin, Xue-Qi Cheng, Tie-Yan Liu

Recurrent neural networks have achieved state-of-the-art results in many artificial intelligence tasks, such as language modeling, neural machine translation, speech recognition and so on.

Language Modelling Machine Translation +3

Limits on Axion Couplings from the first 80-day data of PandaX-II Experiment

no code implementations25 Jul 2017 Changbo Fu, Xiaopeng Zhou, Xun Chen, Yunhua Chen, Xiangyi Cui, Deqing Fang, Karl Giboni, Franco Giuliani, Ke Han, Xingtao Huang, Xiangdong Ji, Yonglin Ju, Siao Lei, Shaoli Li, Huaxuan Liu, Jianglai Liu, Yugang Ma, Yajun Mao, Xiangxiang Ren, Andi Tan, Hongwei Wang, Jimin Wang, Meng Wang, Qiuhong Wang, Siguang Wang, Xuming Wang, Zhou Wang, Shiyong Wu, Mengjiao Xiao, Pengwei Xie, Binbin Yan, Yong Yang, Jianfeng Yue, Hongguang Zhang, Tao Zhang, Li Zhao, Ning Zhou

We report new searches for the solar axions and galactic axion-like dark matter particles, using the first low-background data from PandaX-II experiment at China Jinping Underground Laboratory, corresponding to a total exposure of about $2. 7\times 10^4$ kg$\cdot$day.

High Energy Physics - Experiment Solar and Stellar Astrophysics High Energy Physics - Phenomenology

Adversarial Neural Machine Translation

no code implementations20 Apr 2017 Lijun Wu, Yingce Xia, Li Zhao, Fei Tian, Tao Qin, Jian-Huang Lai, Tie-Yan Liu

The goal of the adversary is to differentiate the translation result generated by the NMT model from that by human.

Machine Translation NMT +1

Somoclu: An Efficient Parallel Library for Self-Organizing Maps

4 code implementations7 May 2013 Peter Wittek, Shi Chao Gao, Ik Soo Lim, Li Zhao

Somoclu is a massively parallel tool for training self-organizing maps on large data sets written in C++.

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