Gang Pan

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Zhejiang University

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Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN

Reconstructing seeing images from fMRI recordings is an absorbing resear...
01/28/2021 ∙ by Tao Fang, et al. ∙ 28 ∙ share

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Sample Complexity of Policy Gradient Finding Second-Order Stationary Points

The goal of policy-based reinforcement learning (RL) is to search the ma...
12/02/2020 ∙ by Long Yang, et al. ∙ 0 ∙ share

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Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks

Address event representation (AER) cameras have recently attracted more ...
02/14/2020 ∙ by Qianhui Liu, et al. ∙ 0 ∙ share

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Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neurons

This paper proposes an unsupervised address event representation (AER) o...
11/19/2019 ∙ by Qianhui Liu, et al. ∙ 24 ∙ share

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Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces

Brain-computer interfaces (BCIs) have enabled prosthetic device control ...
11/02/2019 ∙ by Yu Qi, et al. ∙ 0 ∙ share

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Gradient Q(σ, λ): A Unified Algorithm with Function Approximation for Reinforcement Learning

Full-sampling (e.g., Q-learning) and pure-expectation (e.g., Expected Sa...
09/06/2019 ∙ by Long Yang, et al. ∙ 0 ∙ share

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

In recent years significant progress has been made in dealing with chall...
07/01/2019 ∙ by Longxiang Shi, et al. ∙ 5 ∙ share

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Brain Network Construction and Classification Toolbox (BrainNetClass)

Brain functional network has become an increasingly used approach in und...
06/17/2019 ∙ by Zhen Zhou, et al. ∙ 0 ∙ share

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

Off-policy reinforcement learning with eligibility traces is challenging...
05/17/2019 ∙ by Longxiang Shi, et al. ∙ 0 ∙ share

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

Researchers on artificial intelligence have achieved human-level intelli...
03/22/2019 ∙ by Li Zhang, et al. ∙ 24 ∙ share

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

Researchers on artificial intelligence have achieved human-level intelli...
03/22/2019 ∙ by Li Zhang, et al. ∙ 0 ∙ share

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

Recommendation systems and computing advertisements have gradually enter...
02/25/2019 ∙ by Li Zhang, et al. ∙ 8 ∙ share

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Efficient Spiking Neural Networks with Logarithmic Temporal Coding

A Spiking Neural Network (SNN) can be trained indirectly by first traini...
11/10/2018 ∙ by Ming Zhang, et al. ∙ 0 ∙ share

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Qualitative Measurements of Policy Discrepancy for Return-based Deep Q-Network

In this paper, we focus on policy discrepancy in return-based deep Q-net...
06/14/2018 ∙ by Wenjia Meng, et al. ∙ 0 ∙ share

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Spiking Deep Residual Network

Recently, spiking neural network (SNN) has received significant attentio...
04/28/2018 ∙ by Yangfan Hu, et al. ∙ 0 ∙ share

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State Distribution-aware Sampling for Deep Q-learning

A critical and challenging problem in reinforcement learning is how to l...
04/23/2018 ∙ by Weichao Li, et al. ∙ 0 ∙ share

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A Unified Approach for Multi-step Temporal-Difference Learning with Eligibility Traces in Reinforcement Learning

Recently, a new multi-step temporal learning algorithm, called Q(σ), uni...
02/09/2018 ∙ by Long Yang, et al. ∙ 0 ∙ share

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Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction

Object detection systems based on the deep convolutional neural network ...
04/13/2015 ∙ by Yuting Zhang, et al. ∙ 0 ∙ share

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Robust Face Recognition by Constrained Part-based Alignment

Developing a reliable and practical face recognition system is a long-st...
01/20/2015 ∙ by Yuting Zhang, et al. ∙ 0 ∙ share

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Sparse Principal Component Analysis via Rotation and Truncation

Sparse principal component analysis (sparse PCA) aims at finding a spars...
03/06/2014 ∙ by Zhenfang Hu, et al. ∙ 0 ∙ share

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