Publications (118)
CausalCOMRL: Context-Based Offline Meta-Reinforcement Learning with Causal Representation
Zhengzhe Zhang, Wenjia Meng, Haoliang Sun +1
Context-based offline meta-reinforcement learning (OMRL) methods have achieved appealing success by leveraging pre-collected offline datasets to develop task representations that g…
Off-OAB: Off-Policy Policy Gradient Method with Optimal Action-Dependent Baseline
Wenjia Meng, Qian Zheng, Long Yang +2
Policy-based methods have achieved remarkable success in solving challenging reinforcement learning problems. Among these methods, off-policy policy gradient methods are particular…
Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neurons
Qianhui Liu, Gang Pan, Haibo Ruan +3
This paper proposes an unsupervised address event representation (AER) object recognition approach. The proposed approach consists of a novel multiscale spatio-temporal feature (Mu…
Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces
Yu Qi, Bin Liu, Yueming Wang +1
Brain-computer interfaces (BCIs) have enabled prosthetic device control by decoding motor movements from neural activities. Neural signals recorded from cortex exhibit nonstationar…
Field-aware Neural Factorization Machine for Click-Through Rate Prediction
Li Zhang, Weichen Shen, Shijian Li +1
Recommendation systems and computing advertisements have gradually entered the field of academic research from the field of commercial applications. Click-through rate prediction i…
State Distribution-aware Sampling for Deep Q-learning
Weichao Li, Fuxian Huang, Xi Li +2
A critical and challenging problem in reinforcement learning is how to learn the state-action value function from the experience replay buffer and simultaneously keep sample effici…
LOGO: Video Text Spotting with Language Collaboration and Glyph Perception Model
Hongen Liu, Di Sun, Jiahao Wang +2
Video text spotting (VTS) aims to simultaneously localize, recognize and track text instances in videos. To address the limited recognition capability of end-to-end methods, recent…
VLASCD: A Visual Language Action Model for Simultaneous Chatting and Decision Making
Zuojin Tang, Bin Hu, Chenyang Zhao +3
Recent large pretrained models such as LLMs (e.g., GPT series) and VLAs (e.g., OpenVLA) have achieved notable progress on multimodal tasks, yet they are built upon a multi-input si…
Toward Large-scale Spiking Neural Networks: A Comprehensive Survey and Future Directions
Yangfan Hu, Qian Zheng, Guoqi Li +2
Deep learning has revolutionized artificial intelligence (AI), achieving remarkable progress in fields such as computer vision, speech recognition, and natural language processing.…
CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning
Long Yang, Jiaming Ji, Juntao Dai +3
Safe reinforcement learning (RL) is still very challenging since it requires the agent to consider both return maximization and safe exploration. In this paper, we propose CUP, a C…
CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding
Jiquan Wang, Sha Zhao, Zhiling Luo +5
Electroencephalography (EEG) is a non-invasive technique to measure and record brain electrical activity, widely used in various BCI and healthcare applications. Early EEG decoding…
A Framework For Image Synthesis Using Supervised Contrastive Learning
Yibin Liu, Jianyu Zhang, Li Zhang +2
Text-to-image (T2I) generation aims at producing realistic images corresponding to text descriptions. Generative Adversarial Network (GAN) has proven to be successful in this task.…
A Unified Approach for Multi-step Temporal-Difference Learning with Eligibility Traces in Reinforcement Learning
Long Yang, Minhao Shi, Qian Zheng +2
Recently, a new multi-step temporal learning algorithm, called , unifies -step Tree-Backup (when ) and -step Sarsa (when ) by introducing a sampling parame…
Personalized Sleep Staging Leveraging Source-free Unsupervised Domain Adaptation
Yangxuan Zhou, Sha Zhao, Jiquan Wang +5
Sleep staging is crucial for assessing sleep quality and diagnosing related disorders. Recent deep learning models for automatic sleep staging using polysomnography often suffer fr…
SleepVLM: A Rule-Grounded Vision-Language Model for Auditable Sleep Staging
Guifeng Deng, Pan Wang, Mengfan Niu +9
Sleep staging is essential for sleep assessment and disorder diagnosis. In recent years, automatic sleep staging systems have achieved accuracy approaching that of human experts, b…
SPICED: A Synaptic Homeostasis-Inspired Framework for Unsupervised Continual EEG Decoding
Yangxuan Zhou, Sha Zhao, Jiquan Wang +4
Human brain achieves dynamic stability-plasticity balance through synaptic homeostasis. Inspired by this biological principle, we propose SPICED: a neuromorphic framework that inte…
Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks
Qi Xu, Yaxin Li, Xuanye Fang +4
Spiking neural networks (SNNs) have superb characteristics in sensory information recognition tasks due to their biological plausibility. However, the performance of some current s…
Darwin3: A large-scale neuromorphic chip with a Novel ISA and On-Chip Learning
De Ma, Xiaofei Jin, Shichun Sun +8
Spiking Neural Networks (SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-tem…
TinyLight: Adaptive Traffic Signal Control on Devices with Extremely Limited Resources
Dong Xing, Qian Zheng, Qianhui Liu +1
Recent advances in deep reinforcement learning (DRL) have largely promoted the performance of adaptive traffic signal control (ATSC). Nevertheless, regarding the implementation, mo…
Multi-Depth Branch Network for Efficient Image Super-Resolution
Huiyuan Tian, Li Zhang, Shijian Li +2
A longstanding challenge in Super-Resolution (SR) is how to efficiently enhance high-frequency details in Low-Resolution (LR) images while maintaining semantic coherence. This is p…
A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training
Hanwen Wang, Yu Qi, Lin Yao +3
Brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. En…
Resisting Stochastic Risks in Diffusion Planners with the Trajectory Aggregation Tree
Lang Feng, Pengjie Gu, Bo An +1
Diffusion planners have shown promise in handling long-horizon and sparse-reward tasks due to the non-autoregressive plan generation. However, their inherent stochastic risk of gen…
EvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks
Yangxuan Zhou, Sha Zhao, Jiquan Wang +2
Electroencephalography (EEG) is the cornerstone of non-invasive brain-computer interfaces (BCIs), yet conventional decoding relies on fragmented, task-specific architectures that s…
Hybrid Spiking Vision Transformer for Object Detection with Event Cameras
Qi Xu, Jie Deng, Jiangrong Shen +3
Event-based object detection has gained increasing attention due to its advantages such as high temporal resolution, wide dynamic range, and asynchronous address-event representati…
Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks
Qianhui Liu, Haibo Ruan, Dong Xing +2
Address event representation (AER) cameras have recently attracted more attention due to the advantages of high temporal resolution and low power consumption, compared with traditi…
Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereo
Zongrui Li, Zhan Lu, Haojie Yan +4
Natural Light Uncalibrated Photometric Stereo (NaUPS) relieves the strict environment and light assumptions in classical Uncalibrated Photometric Stereo (UPS) methods. However, due…
Sample Complexity of Policy Gradient Finding Second-Order Stationary Points
Long Yang, Qian Zheng, Gang Pan
The goal of policy-based reinforcement learning (RL) is to search the maximal point of its objective. However, due to the inherent non-concavity of its objective, convergence to a…
Semi-Supervised Pipe Video Temporal Defect Interval Localization
Zhu Huang, Gang Pan, Chao Kang +1
In sewer pipe Closed-Circuit Television (CCTV) inspection, accurate temporal defect localization is essential for effective defect classification, detection, segmentation and quant…
Neural-Driven Image Editing
Pengfei Zhou, Jie Xia, Xiaopeng Peng +15
Traditional image editing typically relies on manual prompting, making it labor-intensive and inaccessible to individuals with limited motor control or language abilities. Leveragi…
DarwinWafer: A Wafer-Scale Neuromorphic Chip
Xiaolei Zhu, Xiaofei Jin, Ziyang Kang +11
Neuromorphic computing promises brain-like efficiency, yet today's multi-chip systems scale over PCBs and incur orders-of-magnitude penalties in bandwidth, latency, and energy, und…
PP-FormulaNet: Bridging Accuracy and Efficiency in Advanced Formula Recognition
Hongen Liu, Cheng Cui, Yuning Du +2
Formula recognition is an important task in document intelligence. It involves converting mathematical expressions from document images into structured symbolic formats that comput…
Copiloting Diagnosis of Autism in Real Clinical Scenarios via LLMs
Yi Jiang, Qingyang Shen, Shuzhong Lai +5
Autism spectrum disorder(ASD) is a pervasive developmental disorder that significantly impacts the daily functioning and social participation of individuals. Despite the abundance…
Roadmap for Unconventional Computing with Nanotechnology
Giovanni Finocchio, Jean Anne C. Incorvia, Joseph S. Friedman +48
In the "Beyond Moore's Law" era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same ti…
Spiking Deep Residual Network
Yangfan Hu, Huajin Tang, Gang Pan
Spiking neural networks (SNNs) have received significant attention for their biological plausibility. SNNs theoretically have at least the same computational power as traditional a…
Efficient ANN-SNN Conversion with Error Compensation Learning
Chang Liu, Jiangrong Shen, Xuming Ran +4
Artificial neural networks (ANNs) have demonstrated outstanding performance in numerous tasks, but deployment in resource-constrained environments remains a challenge due to their…
Darkit: A User-Friendly Software Toolkit for Spiking Large Language Model
Xin Du, Shifan Ye, Qian Zheng +7
Large language models (LLMs) have been widely applied in various practical applications, typically comprising billions of parameters, with inference processes requiring substantial…
Robust Face Recognition by Constrained Part-based Alignment
Yuting Zhang, Kui Jia, Yueming Wang +3
Developing a reliable and practical face recognition system is a long-standing goal in computer vision research. Existing literature suggests that pixel-wise face alignment is the…
LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition
Jinyuan Li, Han Li, Di Sun +4
Grounded Multimodal Named Entity Recognition (GMNER) is a nascent multimodal task that aims to identify named entities, entity types and their corresponding visual regions. GMNER t…
Constrained Update Projection Approach to Safe Policy Optimization
Long Yang, Jiaming Ji, Juntao Dai +5
Safe reinforcement learning (RL) studies problems where an intelligent agent has to not only maximize reward but also avoid exploring unsafe areas. In this study, we propose CUP, a…
VP-MEL: Visual Prompts Guided Multimodal Entity Linking
Hongze Mi, Jinyuan Li, Xuying Zhang +4
Multimodal entity linking (MEL), a task aimed at linking mentions within multimodal contexts to their corresponding entities in a knowledge base (KB), has attracted much attention…
An Asynchronous Multi-core Accelerator for SNN inference
Zhuo Chen, De Ma, Xiaofei Jin +5
Spiking Neural Networks (SNNs) are extensively utilized in brain-inspired computing and neuroscience research. To enhance the speed and energy efficiency of SNNs, several many-core…
FiDi-RL: Incorporating Deep Reinforcement Learning with Finite-Difference Policy Search for Efficient Learning of Continuous Control
Longxiang Shi, Shijian Li, Longbing Cao +3
In recent years significant progress has been made in dealing with challenging problems using reinforcement learning.Despite its great success, reinforcement learning still faces c…
Advancing Grounded Multimodal Named Entity Recognition via LLM-Based Reformulation and Box-Based Segmentation
Jinyuan Li, Ziyan Li, Han Li +4
Grounded Multimodal Named Entity Recognition (GMNER) task aims to identify named entities, entity types and their corresponding visual regions. GMNER task exhibits two challenging…
Mitigating Reward Over-Optimization in RLHF via Behavior-Supported Regularization
Juntao Dai, Taiye Chen, Yaodong Yang +2
Reinforcement learning from human feedback (RLHF) is an effective method for aligning large language models (LLMs) with human values. However, reward over-optimization remains an o…
EMOD: A Unified EEG Emotion Representation Framework Leveraging V-A Guided Contrastive Learning
Yuning Chen, Sha Zhao, Shijian Li +1
Emotion recognition from EEG signals is essential for affective computing and has been widely explored using deep learning. While recent deep learning approaches have achieved stro…
EEGAgent: A Unified Framework for Automated EEG Analysis Using Large Language Models
Sha Zhao, Mingyi Peng, Haiteng Jiang +3
Scalable and generalizable analysis of brain activity is essential for advancing both clinical diagnostics and cognitive research. Electroencephalography (EEG), a non-invasive moda…
Monte Carlo Neural Fictitious Self-Play: Approach to Approximate Nash equilibrium of Imperfect-Information Games
Li Zhang, Wei Wang, Shijian Li +1
Researchers on artificial intelligence have achieved human-level intelligence in large-scale perfect-information games, but it is still a challenge to achieve (nearly) optimal resu…
ActivityForensics: A Comprehensive Benchmark for Localizing Manipulated Activity in Videos
Peijun Bao, Anwei Luo, Gang Pan +2
Temporal forgery localization aims to temporally identify manipulated segments in videos. Most existing benchmarks focus on appearance-level forgeries, such as face swapping and ob…
Gradient Q: A Unified Algorithm with Function Approximation for Reinforcement Learning
Long Yang, Yu Zhang, Qian Zheng +2
Full-sampling (e.g., Q-learning) and pure-expectation (e.g., Expected Sarsa) algorithms are efficient and frequently used techniques in reinforcement learning. Q is the fi…
Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networks
Lang Feng, Qianhui Liu, Huajin Tang +2
Spiking neural networks (SNNs) are bio-inspired neural networks with asynchronous discrete and sparse characteristics, which have increasingly manifested their superiority in low e…
ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks
Jiangrong Shen, Qi Xu, Jian K. Liu +3
Spiking neural networks (SNNs) have manifested remarkable advantages in power consumption and event-driven property during the inference process. To take full advantage of low powe…
Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware
Zhenhui Chen, Haoran Xu, Yangfan Hu +5
Neuromorphic hardware aims to leverage distributed computing and event-driven circuit design to achieve an energy-efficient AI system. The name "neuromorphic" is derived from its s…
LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization
Qianhui Liu, Jiaqi Yan, Malu Zhang +2
Spiking Neural Networks (SNNs) mimic the information-processing mechanisms of the human brain and are highly energy-efficient, making them well-suited for low-power edge devices. H…
BrainBench: Benchmarking Large Language Models for Comprehensive EEG Understanding
Yangxuan Zhou, Sha Zhao, Yuning Chen +4
Electroencephalography (EEG) analysis extends beyond assigning predefined labels to recordings; it requires workflows connecting natural-language instructions, signal processing, q…
Safe Reinforcement Learning using Finite-Horizon Gradient-based Estimation
Juntao Dai, Yaodong Yang, Qian Zheng +1
A key aspect of Safe Reinforcement Learning (Safe RL) involves estimating the constraint condition for the next policy, which is crucial for guiding the optimization of safe policy…
Qualitative Measurements of Policy Discrepancy for Return-Based Deep Q-Network
Wenjia Meng, Qian Zheng, Long Yang +2
The deep Q-network (DQN) and return-based reinforcement learning are two promising algorithms proposed in recent years. DQN brings advances to complex sequential decision problems,…
ALAM: Algebraically Consistent Latent Action Model for Vision-Language-Action Models
Zuojin Tang, Haoyun Liu, Xinyuan Chang +11
Vision-language-action (VLA) models remain constrained by the scarcity of action-labeled robot data, whereas action-free videos provide abundant evidence of how the physical world…
FpgaHub: Fpga-centric Hyper-heterogeneous Computing Platform for Big Data Analytics
Zeke Wang, Jie Zhang, Hongjing Huang +10
Modern data analytics requires a huge amount of computing power and processes a massive amount of data. At the same time, the underlying computing platform is becoming much more he…
Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks
Qi Xu, Yuyuan Gao, Jiangrong Shen +4
Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-temporal patterns in time series, such as the Address-Event Representation data collected from…
NeIF: Representing General Reflectance as Neural Intrinsics Fields for Uncalibrated Photometric Stereo
Zongrui Li, Qian Zheng, Feishi Wang +3
Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by unknown light. Existing solutions alleviate the ambiguity by either explicitly associa…
DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI
Jiquan Wang, Sha Zhao, Yangxuan Zhou +3
Electroencephalography (EEG) foundation models hold significant promise for universal Brain-Computer Interfaces (BCIs). However, existing approaches often rely on end-to-end fine-t…
Rethinking Sampling Strategies for Unsupervised Person Re-identification
Xumeng Han, Xuehui Yu, Guorong Li +5
Unsupervised person re-identification (re-ID) remains a challenging task. While extensive research has focused on the framework design and loss function, this paper shows that samp…
Mitigating Communication Costs in Neural Networks: The Role of Dendritic Nonlinearity
Xundong Wu, Pengfei Zhao, Zilin Yu +6
Our understanding of biological neuronal networks has profoundly influenced the development of artificial neural networks (ANNs). However, neurons utilized in ANNs differ considera…
Otters++: A Time-to-first-spike Based Energy Efficient Optical Spiking Transformer
Zhanglu Yan, Jiayi Mao, Kaiwen Tang +6
Spiking neural networks (SNNs) are promising for energy-efficient inference, and time-to-first-spike (TTFS) coding is especially attractive because each neuron fires at most once.…
Spiking GS: Towards High-Accuracy and Low-Cost Surface Reconstruction via Spiking Neuron-based Gaussian Splatting
Weixing Zhang, Zongrui Li, De Ma +4
3D Gaussian Splatting is capable of reconstructing 3D scenes in minutes. Despite recent advances in improving surface reconstruction accuracy, the reconstructed results still exhib…
Sewer Image Super-Resolution with Depth Priors and Its Lightweight Network
Gang Pan, Chen Wang, Zhijie Sui +5
The Quick-view (QV) technique serves as a primary method for detecting defects within sewerage systems. However, the effectiveness of QV is impeded by the limited visual range of i…
Controlling Type Confounding in Ad Hoc Teamwork with Instance-wise Teammate Feedback Rectification
Dong Xing, Pengjie Gu, Qian Zheng +5
Ad hoc teamwork requires an agent to cooperate with unknown teammates without prior coordination. Many works propose to abstract teammate instances into high-level representation o…
Edge Intelligence with Spiking Neural Networks
Shuiguang Deng, Di Yu, Changze Lv +10
The convergence of artificial intelligence and edge computing has spurred growing interest in enabling intelligent services directly on resource-constrained devices. While traditio…
Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN
Tao Fang, Yu Qi, Gang Pan
Reconstructing seeing images from fMRI recordings is an absorbing research area in neuroscience and provides a potential brain-reading technology. The challenge lies in that visual…
D-Artemis: A Deliberative Cognitive Framework for Mobile GUI Multi-Agents
Hongze Mi, Yibo Feng, Wenjie Lu +12
Graphical User Interface (GUI) agents aim to automate a wide spectrum of human tasks by emulating user interaction. Despite rapid advancements, current approaches are hindered by s…
Design and Validation of a Portable EEG-tES Platform Supporting High-Rate EEG Recording and Temporal Interference Stimulation
Le Xing, Maoxing Liang, Disi A +7
Background: Closed-loop neuromodulation integrating electroencephalography (EEG) and transcranial electrical stimulation (tES) has strong potential for neuroscience research and cl…
SpectralKD: A Unified Framework for Interpreting and Distilling Vision Transformers via Spectral Analysis
Huiyuan Tian, Bonan Xu, Shijian Li +1
Knowledge Distillation (KD) has achieved widespread success in compressing large Vision Transformers (ViTs), but a unified theoretical framework for both ViTs and KD is still lacki…
NeuSort: An Automatic Adaptive Spike Sorting Approach with Neuromorphic Models
Hang Yu, Yu Qi, Gang Pan
Objective. Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims…
Brain-CLIPLM: Semantic Compression for EEG-to-Text Decoding
Xiaoli Yang, Huiyuan Tian, Yurui Li +3
Decoding natural language from non-invasive electroencephalography (EEG) remains constrained by low signal-to-noise ratio and limited information bandwidth. This raises a central q…
EReCu: Pseudo-label Evolution Fusion and Refinement with Multi-Cue Learning for Unsupervised Camouflage Detection
Shuo Jiang, Gaojia Zhang, Min Tan +2
Unsupervised Camouflaged Object Detection (UCOD) remains a challenging task due to the high intrinsic similarity between target objects and their surroundings, as well as the relia…
Spiking NeRF: Representing the Real-World Geometry by a Discontinuous Representation
Zhanfeng Liao, Qian Zheng, Yan Liu +1
A crucial reason for the success of existing NeRF-based methods is to build a neural density field for the geometry representation via multiple perceptron layers (MLPs). MLPs are c…
Otters: An Energy-Efficient SpikingTransformer via Optical Time-to-First-Spike Encoding
Zhanglu Yan, Jiayi Mao, Qianhui Liu +5
Spiking neural networks (SNNs) promise high energy efficiency, particularly with time-to-first-spike (TTFS) encoding, which maximizes sparsity by emitting at most one spike per neu…
Efficient Spiking Neural Networks with Logarithmic Temporal Coding
Ming Zhang, Nenggan Zheng, De Ma +2
A Spiking Neural Network (SNN) can be trained indirectly by first training an Artificial Neural Network (ANN) with the conventional backpropagation algorithm, then converting it in…
SceneCalib: Automatic Targetless Calibration of Cameras and Lidars in Autonomous Driving
Ayon Sen, Gang Pan, Anton Mitrokhin +1
Accurate camera-to-lidar calibration is a requirement for sensor data fusion in many 3D perception tasks. In this paper, we present SceneCalib, a novel method for simultaneous self…
SPAIC: A Spike-based Artificial Intelligence Computing Framework
Chaofei Hong, Mengwen Yuan, Mengxiao Zhang +6
Neuromorphic computing is an emerging research field that aims to develop new intelligent systems by integrating theories and technologies from multi-disciplines such as neuroscien…
Emergence and reconfiguration of modular structure for synaptic neural networks during continual familiarity detection
Shi Gu, Marcelo G Mattar, Huajin Tang +1
While advances in artificial intelligence and neuroscience have enabled the emergence of neural networks capable of learning a wide variety of tasks, our understanding of the tempo…
FP3O: Enabling Proximal Policy Optimization in Multi-Agent Cooperation with Parameter-Sharing Versatility
Lang Feng, Dong Xing, Junru Zhang +1
Existing multi-agent PPO algorithms lack compatibility with different types of parameter sharing when extending the theoretical guarantee of PPO to cooperative multi-agent reinforc…
TD-DPO: Difference-Aware Preference Optimization for Mitigating Sycophancy in Clinical Autism Intervention Dialogue
Shuzhong Lai, Junhong Lai, Chenxi Li +5
The sycophancy of large language models can increase the safety risk in intervention dialogue for autistic children. Supervised fine-tuning can somewhat reduce sycophancy, but rely…
MindGPT: Interpreting What You See with Non-invasive Brain Recordings
Jiaxuan Chen, Yu Qi, Yueming Wang +1
Decoding of seen visual contents with non-invasive brain recordings has important scientific and practical values. Efforts have been made to recover the seen images from brain sign…
Optimize Neural Fictitious Self-Play in Regret Minimization Thinking
Yuxuan Chen, Li Zhang, Shijian Li +1
Optimization of deep learning algorithms to approach Nash Equilibrium remains a significant problem in imperfect information games, e.g. StarCraft and poker. Neural Fictitious Self…
Bidirectional Distillation: A Mixed-Play Framework for Multi-Agent Generalizable Behaviors
Lang Feng, Jiahao Lin, Dong Xing +3
Population-population generalization is a challenging problem in multi-agent reinforcement learning (MARL), particularly when agents encounter unseen co-players. However, existing…
CTC: The Composite Task Challenge for Cooperative Multi-Agent Reinforcement Learning
Yurui Li, Yuxuan Chen, Li Zhang +3
The critical role of division of labor (DOL) in enhancing cooperation is well-recognized in real-world applications. Consequently, many cooperative multi-agent reinforcement learni…
Spectral Sparse Representation for Clustering: Evolved from PCA, K-means, Laplacian Eigenmap, and Ratio Cut
Zhenfang Hu, Gang Pan, Yueming Wang +1
Dimensionality reduction, cluster analysis, and sparse representation are basic components in machine learning. However, their relationships have not yet been fully investigated. I…
DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering
Zongrui Li, Qian Zheng, Boxin Shi +2
Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light. Although the ambiguity is alleviated on non-Lambertian objects, the…
UniSpike: Accelerating Spiking Neural Networks on Neuromorphic Systems via Eliminating Address Redundancy
Qinghui Xing, Zhuo Chen, Xin Du +6
Many-core neuromorphic systems accelerate Spiking Neural Networks (SNNs), yet their packet-based spike communication can spend substantial traffic and energy repeatedly transmittin…
Enhancing SNN-based Spatio-Temporal Learning: A Benchmark Dataset and Cross-Modality Attention Model
Shibo Zhou, Bo Yang, Mengwen Yuan +4
Spiking Neural Networks (SNNs), renowned for their low power consumption, brain-inspired architecture, and spatio-temporal representation capabilities, have garnered considerable a…
Brain Network Construction and Classification Toolbox (BrainNetClass)
Zhen Zhou, Xiaobo Chen, Yu Zhang +5
Brain functional network has become an increasingly used approach in understanding brain functions and diseases. Many network construction methods have been developed, whereas the…
TBQ(): Improving Efficiency of Trace Utilization for Off-Policy Reinforcement Learning
Longxiang Shi, Shijian Li, Longbing Cao +2
Off-policy reinforcement learning with eligibility traces is challenging because of the discrepancy between target policy and behavior policy. One common approach is to measure the…
Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency
Jiangrong Shen, Qi Xu, Gang Pan +1
The human brain utilizes spikes for information transmission and dynamically reorganizes its network structure to boost energy efficiency and cognitive capabilities throughout its…
Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation
Qi Xu, Yaxin Li, Jiangrong Shen +3
Spiking neural networks (SNNs) are well known as the brain-inspired models with high computing efficiency, due to a key component that they utilize spikes as information units, clo…
BrainAgent: A Large Language Model-Driven Multi-Agent Framework for Autonomous Brain Signal Understanding
Yangxuan Zhou, Sha Zhao, Jiquan Wang +2
Brain-Computer Interfaces (BCIs) and brain signal understanding are pivotal for clinical health and next-generation interactions. Despite this significance, its widespread adoption…
Wearable Music2Emotion : Assessing Emotions Induced by AI-Generated Music through Portable EEG-fNIRS Fusion
Sha Zhao, Song Yi, Yangxuan Zhou +6
Emotions critically influence mental health, driving interest in music-based affective computing via neurophysiological signals with Brain-computer Interface techniques. While prio…
Thompson Sampling for Unimodal Bandits
Long Yang, Zhao Li, Zehong Hu +4
In this paper, we propose a Thompson Sampling algorithm for \emph{unimodal} bandits, where the expected reward is unimodal over the partially ordered arms. To exploit the unimodal…
Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning
Yaxin Li, Qi Xu, Jiangrong Shen +3
The emergence of deep and large-scale spiking neural networks (SNNs) exhibiting high performance across diverse complex datasets has led to a need for compressing network models du…
TS-SNN: Temporal Shift Module for Spiking Neural Networks
Kairong Yu, Tianqing Zhang, Qi Xu +2
Spiking Neural Networks (SNNs) are increasingly recognized for their biological plausibility and energy efficiency, positioning them as strong alternatives to Artificial Neural Net…