Search Results for author: Lingwei Zhu

Found 15 papers, 1 papers with code

Drugs Resistance Analysis from Scarce Health Records via Multi-task Graph Representation

no code implementations22 Feb 2023 Honglin Shu, Pei Gao, Lingwei Zhu, Zheng Chen

In this paper, we propose a novel framework for rapid clinical intervention by viewing health records as graphs whose nodes are mapped from medical events and edges as correspondence between events in given a time window.

Multi-Task Learning

Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence

no code implementations27 Jan 2023 Lingwei Zhu, Zheng Chen, Takamitsu Matsubara, Martha White

Many policy optimization approaches in reinforcement learning incorporate a Kullback-Leilbler (KL) divergence to the previous policy, to prevent the policy from changing too quickly.

Atari Games reinforcement-learning +1

Cyclic Policy Distillation: Sample-Efficient Sim-to-Real Reinforcement Learning with Domain Randomization

no code implementations29 Jul 2022 Yuki Kadokawa, Lingwei Zhu, Yoshihisa Tsurumine, Takamitsu Matsubara

Deep reinforcement learning with domain randomization learns a control policy in various simulations with randomized physical and sensor model parameters to become transferable to the real world in a zero-shot setting.

reinforcement-learning Reinforcement Learning (RL)

Cancer Subtyping by Improved Transcriptomic Features Using Vector Quantized Variational Autoencoder

no code implementations20 Jul 2022 Zheng Chen, Ziwei Yang, Lingwei Zhu, Guang Shi, Kun Yue, Takashi Matsubara, Shigehiko Kanaya, MD Altaf-Ul-Amin

As such, existing methods often impose unrealistic assumptions to extract useful features from the data while avoiding overfitting to spurious correlations.

Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization

1 code implementation22 Jun 2022 Zheng Chen, Lingwei Zhu, Ziwei Yang, Takashi Matsubara

Cancer subtyping is crucial for understanding the nature of tumors and providing suitable therapy.


$q$-Munchausen Reinforcement Learning

no code implementations16 May 2022 Lingwei Zhu, Zheng Chen, Eiji Uchibe, Takamitsu Matsubara

The recently successful Munchausen Reinforcement Learning (M-RL) features implicit Kullback-Leibler (KL) regularization by augmenting the reward function with logarithm of the current stochastic policy.

reinforcement-learning Reinforcement Learning (RL)

Enforcing KL Regularization in General Tsallis Entropy Reinforcement Learning via Advantage Learning

no code implementations16 May 2022 Lingwei Zhu, Zheng Chen, Eiji Uchibe, Takamitsu Matsubara

Maximum Tsallis entropy (MTE) framework in reinforcement learning has gained popularity recently by virtue of its flexible modeling choices including the widely used Shannon entropy and sparse entropy.

reinforcement-learning Reinforcement Learning (RL)

Multi-Tier Platform for Cognizing Massive Electroencephalogram

no code implementations21 Apr 2022 Zheng Chen, Lingwei Zhu, Ziwei Yang, Renyuan Zhang

A spiking neural network (SNN) based tier is designed to distill the principle information in terms of spike-streams from the rare features, which maintains the temporal implication in the nature of EEGs.

Electroencephalogram (EEG)

Automated Sleep Staging via Parallel Frequency-Cut Attention

no code implementations7 Apr 2022 Zheng Chen, Ziwei Yang, Lingwei Zhu, Wei Chen, Toshiyo Tamura, Naoaki Ono, MD Altaf-Ul-Amin, Shigehiko Kanaya, Ming Huang

This paper proposes a novel framework for automatically capturing the time-frequency nature of electroencephalogram (EEG) signals of human sleep based on the authoritative sleep medicine guidance.

Decision Making Electroencephalogram (EEG) +2

Adaptive Spike-Like Representation of EEG Signals for Sleep Stages Scoring

no code implementations2 Apr 2022 Lingwei Zhu, Koki Odani, Ziwei Yang, Guang Shi, Yirong Kan, Zheng Chen, Renyuan Zhang

Recently there has seen promising results on automatic stage scoring by extracting spatio-temporal features from electroencephalogram (EEG).

Electroencephalogram (EEG) Feature Engineering

Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics Data

no code implementations2 Apr 2022 Ziwei Yang, Lingwei Zhu, Zheng Chen, Ming Huang, Naoaki Ono, MD Altaf-Ul-Amin, Shigehiko Kanaya

In this paper, we propose to investigate automatic subtyping from an unsupervised learning perspective by directly constructing the underlying data distribution itself, hence sufficient data can be generated to alleviate the issue of overfitting.


Geometric Value Iteration: Dynamic Error-Aware KL Regularization for Reinforcement Learning

no code implementations16 Jul 2021 Toshinori Kitamura, Lingwei Zhu, Takamitsu Matsubara

The recent boom in the literature on entropy-regularized reinforcement learning (RL) approaches reveals that Kullback-Leibler (KL) regularization brings advantages to RL algorithms by canceling out errors under mild assumptions.

reinforcement-learning Reinforcement Learning (RL)

Cautious Policy Programming: Exploiting KL Regularization in Monotonic Policy Improvement for Reinforcement Learning

no code implementations13 Jul 2021 Lingwei Zhu, Toshinori Kitamura, Takamitsu Matsubara

In this paper, we propose cautious policy programming (CPP), a novel value-based reinforcement learning (RL) algorithm that can ensure monotonic policy improvement during learning.

Atari Games reinforcement-learning +1

Cautious Actor-Critic

no code implementations12 Jul 2021 Lingwei Zhu, Toshinori Kitamura, Takamitsu Matsubara

The oscillating performance of off-policy learning and persisting errors in the actor-critic (AC) setting call for algorithms that can conservatively learn to suit the stability-critical applications better.

Continuous Control

Ensuring Monotonic Policy Improvement in Entropy-regularized Value-based Reinforcement Learning

no code implementations25 Aug 2020 Lingwei Zhu, Takamitsu Matsubara

We propose a novel reinforcement learning algorithm that exploits this lower-bound as a criterion for adjusting the degree of a policy update for alleviating policy oscillation.

reinforcement-learning Reinforcement Learning (RL)

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