Search Results for author: Dongqi Han

Found 18 papers, 10 papers with code

Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators

1 code implementation23 May 2024 Changze Lv, Dongqi Han, Yansen Wang, Xiaoqing Zheng, Xuanjing Huang, Dongsheng Li

Spiking neural networks (SNNs) represent a promising approach to developing artificial neural networks that are both energy-efficient and biologically plausible.

Image Classification text-classification +3

Understanding and Improving Training-free Loss-based Diffusion Guidance

1 code implementation19 Mar 2024 Yifei Shen, Xinyang Jiang, Yezhen Wang, Yifan Yang, Dongqi Han, Dongsheng Li

Adding additional control to pretrained diffusion models has become an increasingly popular research area, with extensive applications in computer vision, reinforcement learning, and AI for science.

Motion Generation

Efficient and Effective Time-Series Forecasting with Spiking Neural Networks

1 code implementation2 Feb 2024 Changze Lv, Yansen Wang, Dongqi Han, Xiaoqing Zheng, Xuanjing Huang, Dongsheng Li

In this paper, we propose a framework for SNNs in time-series forecasting tasks, leveraging the efficiency of spiking neurons in processing temporal information.

Model Selection Time Series +1

Toward Open-ended Embodied Tasks Solving

no code implementations10 Dec 2023 William Wei Wang, Dongqi Han, Xufang Luo, Yifei Shen, Charles Ling, Boyu Wang, Dongsheng Li

Empowering embodied agents, such as robots, with Artificial Intelligence (AI) has become increasingly important in recent years.

AdaMedGraph: Adaboosting Graph Neural Networks for Personalized Medicine

no code implementations24 Nov 2023 Jie Lian, Xufang Luo, Caihua Shan, Dongqi Han, Varut Vardhanabhuti, Dongsheng Li

However, selecting the appropriate edge feature to define patient similarity and construct the graph is challenging, given that each patient is depicted by high-dimensional features from diverse sources.

Energy-Efficient Visual Search by Eye Movement and Low-Latency Spiking Neural Network

no code implementations10 Oct 2023 Yunhui Zhou, Dongqi Han, Yuguo Yu

Here, we carry out experiments to examine human visual search behaviors and establish the first SNN-based visual search model.

Habits and goals in synergy: a variational Bayesian framework for behavior

1 code implementation11 Apr 2023 Dongqi Han, Kenji Doya, Dongsheng Li, Jun Tani

The habitual behavior is generated by using prior distribution of intention, which is goal-less; and the goal-directed behavior is generated by the posterior distribution of intention, which is conditioned on the goal.

DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications

1 code implementation23 Sep 2021 Dongqi Han, Zhiliang Wang, Wenqi Chen, Ying Zhong, Su Wang, Han Zhang, Jiahai Yang, Xingang Shi, Xia Yin

Experimental results show that DeepAID can provide high-quality interpretations for unsupervised DL models while meeting the special requirements of security domains.

Anomaly Detection

Goal-Directed Planning by Reinforcement Learning and Active Inference

no code implementations18 Jun 2021 Dongqi Han, Kenji Doya, Jun Tani

Habitual behavior, which is obtained from the prior distribution of ${z}$, is acquired by reinforcement learning.

Bayesian Inference Decision Making +3

Learning Memory-Dependent Continuous Control from Demonstrations

no code implementations18 Feb 2021 Siqing Hou, Dongqi Han, Jun Tani

This paper builds on the idea of replaying demonstrations for memory-dependent continuous control, by proposing a novel algorithm, Recurrent Actor-Critic with Demonstration and Experience Replay (READER).

continuous-control Continuous Control +5

Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks

1 code implementation NeurIPS 2020 Dongqi Han, Erik De Schutter, Sungho Hong

This mechanism boosts information transfer carried by a propagating spike signal and thereby supports reliable spike signal and information transmission in a deep FFN.

Variational Recurrent Models for Solving Partially Observable Control Tasks

1 code implementation ICLR 2020 Dongqi Han, Kenji Doya, Jun Tani

In partially observable (PO) environments, deep reinforcement learning (RL) agents often suffer from unsatisfactory performance, since two problems need to be tackled together: how to extract information from the raw observations to solve the task, and how to improve the policy.

Deep Reinforcement Learning Memorization +1

Gap-Increasing Policy Evaluation for Efficient and Noise-Tolerant Reinforcement Learning

no code implementations18 Jun 2019 Tadashi Kozuno, Dongqi Han, Kenji Doya

We provide detailed theoretical analysis of the new algorithm that shows its efficiency and noise-tolerance inherited from Retrace and advantage learning.

reinforcement-learning Reinforcement Learning +1

Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks

1 code implementation29 Jan 2019 Dongqi Han, Kenji Doya, Jun Tani

Furthermore, we show that the self-developed compositionality of the network enhances faster re-learning when adapting to a new task that is a re-composition of previously learned sub-goals, than when starting from scratch.

continuous-control Continuous Control +3

Cannot find the paper you are looking for? You can Submit a new open access paper.