Search Results for author: Shijian Li

Found 15 papers, 5 papers with code

Generalizable Sleep Staging via Multi-Level Domain Alignment

1 code implementation13 Dec 2023 Jiquan Wang, Sha Zhao, Haiteng Jiang, Shijian Li, Tao Li, Gang Pan

In this paper, we introduce domain generalization into automatic sleep staging and propose the task of generalizable sleep staging which aims to improve the model generalization ability to unseen datasets.

Domain Generalization Sleep Staging

Multi-Depth Branch Network for Efficient Image Super-Resolution

1 code implementation29 Sep 2023 Huiyuan Tian, Li Zhang, Shijian Li, Min Yao, Gang Pan

We visualize this process using feature maps, and further demonstrate the rationality and effectiveness of this design using proposed novel Fourier spectral analysis methods.

Image Super-Resolution

A detail-enhanced sampling strategy in Hadamard single-pixel imaging

no code implementations9 Sep 2022 Yan Cai, Shijian Li, Wei zhang, Hao Wu, Xu-Ri Yao, Qing Zhao

Hadamard single-pixel imaging (HSI) is an appealing imaging technique due to its features of low hardware complexity and industrial cost.

Image Reconstruction

Thompson Sampling for Unimodal Bandits

no code implementations15 Jun 2021 Long Yang, Zhao Li, Zehong Hu, Shasha Ruan, Shijian Li, Gang Pan, Hongyang Chen

In this paper, we propose a Thompson Sampling algorithm for \emph{unimodal} bandits, where the expected reward is unimodal over the partially ordered arms.

Thompson Sampling

Optimize Neural Fictitious Self-Play in Regret Minimization Thinking

no code implementations22 Apr 2021 Yuxuan Chen, Li Zhang, Shijian Li, Gang Pan

Optimization of deep learning algorithms to approach Nash Equilibrium remains a significant problem in imperfect information games, e. g. StarCraft and poker.

Starcraft

Sync-Switch: Hybrid Parameter Synchronization for Distributed Deep Learning

1 code implementation16 Apr 2021 Shijian Li, Oren Mangoubi, Lijie Xu, Tian Guo

Further, we observe that Sync-Switch achieves 3. 8% higher converged accuracy with just 1. 23X the training time compared to training with ASP.

Characterizing and Modeling Distributed Training with Transient Cloud GPU Servers

1 code implementation7 Apr 2020 Shijian Li, Robert J. Walls, Tian Guo

However, it is challenging to determine the appropriate cluster configuration---e. g., server type and number---for different training workloads while balancing the trade-offs in training time, cost, and model accuracy.

Perseus: Characterizing Performance and Cost of Multi-Tenant Serving for CNN Models

1 code implementation5 Dec 2019 Matthew LeMay, Shijian Li, Tian Guo

Leveraging Perseus, we evaluated the inference throughput and cost for serving various models and demonstrated that multi-tenant model serving led to up to 12% cost reduction.

Inverse Reinforcement Learning with Multiple Ranked Experts

no code implementations31 Jul 2019 Pablo Samuel Castro, Shijian Li, Daqing Zhang

We consider the problem of learning to behave optimally in a Markov Decision Process when a reward function is not specified, but instead we have access to a set of demonstrators of varying performance.

reinforcement-learning Reinforcement Learning (RL)

FiDi-RL: Incorporating Deep Reinforcement Learning with Finite-Difference Policy Search for Efficient Learning of Continuous Control

no code implementations1 Jul 2019 Longxiang Shi, Shijian Li, Longbing Cao, Long Yang, Gang Zheng, Gang Pan

Alternatively, derivative-based methods treat the optimization process as a blackbox and show robustness and stability in learning continuous control tasks, but not data efficient in learning.

Continuous Control reinforcement-learning +1

TBQ($σ$): Improving Efficiency of Trace Utilization for Off-Policy Reinforcement Learning

no code implementations17 May 2019 Longxiang Shi, Shijian Li, Longbing Cao, Long Yang, Gang Pan

However, existing off-policy learning methods based on probabilistic policy measurement are inefficient when utilizing traces under a greedy target policy, which is ineffective for control problems.

reinforcement-learning Reinforcement Learning (RL)

Monte Carlo Neural Fictitious Self-Play: Approach to Approximate Nash equilibrium of Imperfect-Information Games

no code implementations22 Mar 2019 Li Zhang, Wei Wang, Shijian Li, Gang Pan

Experimentally, we demonstrate that the proposed Monte Carlo Neural Fictitious Self Play can converge to approximate Nash equilibrium in games with large-scale search depth while the Neural Fictitious Self Play can't.

Speeding up Deep Learning with Transient Servers

no code implementations28 Feb 2019 Shijian Li, Robert J. Walls, Lijie Xu, Tian Guo

Distributed training frameworks, like TensorFlow, have been proposed as a means to reduce the training time of deep learning models by using a cluster of GPU servers.

Field-aware Neural Factorization Machine for Click-Through Rate Prediction

no code implementations25 Feb 2019 Li Zhang, Weichen Shen, Shijian Li, Gang Pan

This model can have strong second order feature interactive learning ability like Field-aware Factorization Machine, on this basis, deep neural network is used for higher-order feature combination learning.

Click-Through Rate Prediction Feature Engineering +1

Algorithmic Collusion in Cournot Duopoly Market: Evidence from Experimental Economics

no code implementations21 Feb 2018 Nan Zhou, Li Zhang, Shijian Li, Zhijian Wang

In application, we hope, the frameworks, the algorithm design as well as the experiment environment illustrated in this work, can be an incubator or a test bed for researchers and policymakers to handle the emerging algorithmic collusion.

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