no code implementations • 24 May 2022 • Jun Yang, Krzysztof Łatuszyński, Gareth O. Roberts
High dimensional distributions, especially those with heavy tails, are notoriously difficult for off-the-shelf MCMC samplers: the combination of unbounded state spaces, diminishing gradient information, and local moves, results in empirically observed "stickiness" and poor theoretical mixing properties -- lack of geometric ergodicity.
no code implementations • 15 May 2022 • Hai Yang, Yibin Liu, Junxiao Wang, Jun Yang
In this paper, an improved multi-step finite control set model predictive current control (FCS-MPCC) strategy with speed loop disturbance compensation is proposed for permanent magnet synchronous machine (PMSM) drives system.
no code implementations • 16 Mar 2022 • Yuan Tan, Jun Yang, Wen-Hua Chen, Shihua Li
In this work, we propose a distributionally robust stochastic model predictive control (DR-SMPC) algorithm to address the problem of two-sided chance constrained discrete-time linear system corrupted by additive noise.
no code implementations • 12 Mar 2022 • Yuan Tan, Jun Yang, Wen-Hua Chen, Shihua Li
Motivated by the recently proposed dual control for exploration and exploitation (DCEE) concept, this paper presents a Multi-Step DCEE (MS-DCEE) framework with guaranteed convergence for autonomous search of a source of airborne dispersion.
no code implementations • 27 Feb 2022 • Jun Yang, Steven L. Waslander
Depth acquisition with the active stereo camera is a challenging task for highly reflective objects.
no code implementations • 15 Nov 2021 • Qiyue Yin, Jun Yang, Wancheng Ni, Bin Liang, Kaiqi Huang
With breakthrough of AlphaGo, AI in human-computer game has become a very hot topic attracting researchers all around the world, which usually serves as an effective standard for testing artificial intelligence.
no code implementations • 29 Oct 2021 • Maoyu Mao, Jun Yang
This scheme extracts two types of minute information hidden between face pixels-photoplethysmography (PPG) features and auto-regressive (AR) features, which are used as the basis for forensics in the temporal and spatial domains, respectively.
no code implementations • ICLR 2022 • Xiaoteng Ma, Yiqin Yang, Hao Hu, Qihan Liu, Jun Yang, Chongjie Zhang, Qianchuan Zhao, Bin Liang
Offline reinforcement learning (RL) shows promise of applying RL to real-world problems by effectively utilizing previously collected data.
no code implementations • 29 Sep 2021 • Mingyang Liu, Chengjie WU, Qihan Liu, Yansen Jing, Jun Yang, Pingzhong Tang, Chongjie Zhang
Search algorithms have been playing a vital role in the success of superhuman AI in both perfect information and imperfect information games.
no code implementations • 18 Aug 2021 • Zhongguo Li, Wen-Hua Chen, Jun Yang
In this paper, a concurrent learning framework is developed for source search in an unknown environment using autonomous platforms equipped with onboard sensors.
no code implementations • 16 Jul 2021 • Jiandong Mu, Mengdi Wang, Feiwen Zhu, Jun Yang, Wei Lin, Wei zhang
Reinforcement learning (RL)-based auto-pruning has been further proposed to automate the DNN pruning process to avoid expensive hand-crafted work.
no code implementations • 7 Jun 2021 • Xiaoteng Ma, Xiaohang Tang, Li Xia, Jun Yang, Qianchuan Zhao
Our work provides a unified framework of the trust region approach including both the discounted and average criteria, which may complement the framework of reinforcement learning beyond the discounted objectives.
1 code implementation • NeurIPS 2021 • Yiqin Yang, Xiaoteng Ma, Chenghao Li, Zewu Zheng, Qiyuan Zhang, Gao Huang, Jun Yang, Qianchuan Zhao
Moreover, we extend ICQ to multi-agent tasks by decomposing the joint-policy under the implicit constraint.
1 code implementation • NeurIPS 2021 • Chenghao Li, Tonghan Wang, Chengjie WU, Qianchuan Zhao, Jun Yang, Chongjie Zhang
Recently, deep multi-agent reinforcement learning (MARL) has shown the promise to solve complex cooperative tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
1 code implementation • 4 Jun 2021 • Shaokun Zhang, Xiawu Zheng, Chenyi Yang, Yuchao Li, Yan Wang, Fei Chao, Mengdi Wang, Shen Li, Jun Yang, Rongrong Ji
Motivated by the necessity of efficient inference across various constraints on BERT, we propose a novel approach, YOCO-BERT, to achieve compress once and deploy everywhere.
no code implementations • 17 May 2021 • Jun Yang, Nico Brailovsky
This paper proposes a dual-stage, low complexity, and reconfigurable technique to enhance the speech contaminated by various types of noise sources.
no code implementations • 17 Feb 2021 • Julian L. Pita Ruiz, Lucas G. Rocha, Jun Yang, Sukru Ekin Kocabas, Ming-Jun Li, Ivan Aldaya, Paulo Dainese, Lucas H. Gabrielli
Compact fiber-to-chip couplers play an important role in optical interconnections, especially in data centers.
Optics Applied Physics
no code implementations • 29 Jan 2021 • Xuecong Sun, Han Jia, Yuzhen Yang, Han Zhao, Yafeng Bi, Zhaoyong Sun, Jun Yang
From ancient to modern times, acoustic structures have been used to control the propagation of acoustic waves.
no code implementations • 30 Nov 2020 • Shuang Wang, Jun Yang
This study improves the understanding of wave--mean flow interactions on tidally locked planets.
Atmospheric and Oceanic Physics Earth and Planetary Astrophysics Fluid Dynamics
no code implementations • 28 Oct 2020 • Yiwu Yao, Yuchao Li, Chengyu Wang, Tianhang Yu, Houjiang Chen, Xiaotang Jiang, Jun Yang, Jun Huang, Wei Lin, Hui Shu, Chengfei Lv
The intensive computation of Automatic Speech Recognition (ASR) models obstructs them from being deployed on mobile devices.
no code implementations • 30 Sep 2020 • Jun Yang, Qing Li, Yixuan Sun
Tailings ponds are places for storing industrial waste.
no code implementations • 23 Sep 2020 • Zhen Zheng, Pengzhan Zhao, Guoping Long, Feiwen Zhu, Kai Zhu, Wenyi Zhao, Lansong Diao, Jun Yang, Wei. Lin
We show in this work that memory intensive computations can result in severe performance problems due to off-chip memory access and CPU-GPU context switch overheads in a wide range of deep learning models.
no code implementations • 7 Sep 2020 • Krzysztof Łakomy, Rafal Madonski, Bin Dai, Jun Yang, Piotr Kicki, Maral Ansari, Shihua Li
The performance of active disturbance rejection control (ADRC) algorithms can be limited in practice by high-frequency measurement noise.
no code implementations • 28 Jul 2020 • Yu-Jun Zheng, Si-Lan Yu, Jun-Chao Yang, Tie-Er Gan, Qin Song, Jun Yang, Mumtaz Karatas
First, we use a fuzzy clustering method to divide the population based on both modern medicine and TCM health characteristics; we then use an interactive optimization method, in which TCM experts develop different TCM prevention programs for different clusters, and a heuristic algorithm is used to optimize the programs under the resource constraints.
no code implementations • 8 Jul 2020 • Siyu Wang, Yi Rong, Shiqing Fan, Zhen Zheng, Lansong Diao, Guoping Long, Jun Yang, Xiaoyong Liu, Wei. Lin
The last decade has witnessed growth in the computational requirements for training deep neural networks.
no code implementations • 25 Jun 2020 • Chenghao Li, Xiaoteng Ma, Chongjie Zhang, Jun Yang, Li Xia, Qianchuan Zhao
In these tasks, our approach learns a diverse set of options, each of whose state-action space has strong coherence.
no code implementations • 11 Jun 2020 • Lianmin Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody Hao Yu, Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, Koushik Sen, Joseph E. Gonzalez, Ion Stoica
Ansor can find high-performance programs that are outside the search space of existing state-of-the-art approaches.
1 code implementation • 5 Jun 2020 • Ming Zhang, Yawei Wang, Xiaoteng Ma, Li Xia, Jun Yang, Zhiheng Li, Xiu Li
The generative adversarial imitation learning (GAIL) has provided an adversarial learning framework for imitating expert policy from demonstrations in high-dimensional continuous tasks.
no code implementations • 31 May 2020 • Qinggang Zhou, Yawen Zhang, Pengcheng Li, Xiaoyong Liu, Jun Yang, Runsheng Wang, Ru Huang
The state-of-the-art deep learning algorithms rely on distributed training systems to tackle the increasing sizes of models and training data sets.
no code implementations • 30 Apr 2020 • Xiaoteng Ma, Li Xia, Zhengyuan Zhou, Jun Yang, Qianchuan Zhao
In this paper, we present a new reinforcement learning (RL) algorithm called Distributional Soft Actor Critic (DSAC), which exploits the distributional information of accumulated rewards to achieve better performance.
no code implementations • 25 Mar 2020 • Jun Yang, Fei Wang
The advantage of this method is to make the model converge to various local optima by scheduling the learning rate in once training.
no code implementations • 14 Oct 2019 • Mengdi Wang, Chen Meng, Guoping Long, Chuan Wu, Jun Yang, Wei. Lin, Yangqing Jia
One critical issue for efficiently operating practical AI clouds, is to characterize the computing and data transfer demands of these workloads, and more importantly, the training performance given the underlying software framework and hardware configurations.
no code implementations • NeurIPS 2019 • Jun Yang, Shengyang Sun, Daniel M. Roy
The developments of Rademacher complexity and PAC-Bayesian theory have been largely independent.
no code implementations • 23 Jul 2019 • Junyang Gao, Xi-An Li, Yifan Ethan Xu, Bunyamin Sisman, Xin Luna Dong, Jun Yang
To address the problem, this paper proposes an efficient sampling and evaluation framework, which aims to provide quality accuracy evaluation with strong statistical guarantee while minimizing human efforts.
Databases
no code implementations • 21 Nov 2018 • Mengdi Wang, Qing Zhang, Jun Yang, Xiaoyuan Cui, Wei. Lin
In this method, the network is viewed as a computational graph, in which the vertices denote the computation nodes and edges represent the information flow.
no code implementations • 21 Nov 2018 • Wanchen Sui, Qing Zhang, Jun Yang, Wei Chu
In this paper, we propose a novel integrated framework for learning both text detection and recognition.
no code implementations • 20 Nov 2018 • Donghui Sun, Qing Zhang, Jun Yang
The generator is built using one encoder-decoder structure with cascaded refinement connections and mirror skip connections.
no code implementations • 13 Nov 2018 • Guoping Long, Jun Yang, Kai Zhu, Wei. Lin
In recent years, there is a surge on machine learning applications in industry.
Distributed, Parallel, and Cluster Computing Mathematical Software
no code implementations • 25 Oct 2018 • Min Chen, Jun Zhou, Guangming Tao, Jun Yang, Long Hu
The learning algorithm for the life modeling embedded in Fitbot can achieve better user's experience of affective social interaction.
EEG
Human-Computer Interaction
1 code implementation • Elsevier 2017 • Xin Yang, Chaoyue Liu, Zhiwei Wang, Jun Yang, Hung Le Min, Liang Wang, Kwang-Ting (Tim) Cheng
Each network is trained using images of a single modality in a weakly-supervised manner by providing a set of prostate images with image-level labels indicating only the presence of PCa without priors of lesions’ locations.
no code implementations • 1 May 2017 • Zhaocai Sun, William K. Cheung, Xiaofeng Zhang, Jun Yang
This issue is known as model misspecification.
no code implementations • 16 Apr 2015 • Jun Yang, Qingsong Wei, Cheng Chen, Chundong Wang, and Khai Leong Yong, Data Storage Institute, A-STAR; Bingsheng He, Nanyang Technological University
Although the memory fence and CPU cacheline flush instructions can order memory writes to achieve data consistency, they introduce a significant overhead (more than 10X slower in performance).