1.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Zhejiang UniversityCN156 papers
- Chinese Academy of SciencesCN141 papers
- University of Chinese Academy of SciencesCN122 papers
- National Astronomical ObservatoriesCN107 papers
- Nanjing UniversityCN69 papers
- Peking UniversityCN64 papers
- Tsinghua UniversityCN54 papers
- Shanghai Astronomical ObservatoryCN53 papers
- Beijing Normal UniversityCN50 papers
- Purple Mountain ObservatoryCN50 papers
- University of Science and Technology of ChinaCN49 papers
- Shanghai Jiao Tong UniversityCN41 papers
27 papers · 1 filter
Task-Relevant Representation Decoupling for Visual Reinforcement Learning Generalization
Jinwen Wang, Youfang Lin, Xiaobo Hu +4
Visual Reinforcement Learning (VRL) has achieved considerable success in solving control tasks. However, generalizing learned policies to new environments remains a major challenge…
Catching Every Ripple: Enhanced Anomaly Awareness via Dynamic Concept Adaptation
Jiaqi Zhu, Shaofeng Cai, Jie Chen +3
Online anomaly detection (OAD) plays a pivotal role in real-time analytics and decision-making for evolving data streams. However, existing methods often rely on costly retraining…
Live or Lie: Action-Aware Capsule Multiple Instance Learning for Risk Assessment in Live Streaming Platforms
Yiran Qiao, Jing Chen, Xiang Ao +3
Live streaming has become a cornerstone of today's internet, enabling massive real-time social interactions. However, it faces severe risks arising from sparse, coordinated malicio…
In-Context Source and Channel Coding
Ziqiong Wang, Tianqi Ren, Rongpeng Li +2
Separate Source-Channel Coding (SSCC) remains attractive for text transmission due to its modularity and compatibility with mature entropy coders and powerful channel codes. Howeve…
Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning
Bin Li, Xiaoye Miao, Yan Zhang +1
Decentralized federated learning (DFL) is inherently vulnerable to data poisoning attacks, as malicious clients can transmit manipulated gradients to neighboring clients. Existing…
CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models
Huiwen Wu, Xiaogang Xu, Deyi Zhang +3
The success of current Large-Language Models (LLMs) hinges on extensive training data that is collected and stored centrally, called Centralized Learning (CL). However, such a coll…
Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting
Tianxiang Zhan, Yuanpeng He, Yong Deng +3
In practical scenarios, time series forecasting necessitates not only accuracy but also efficiency. Consequently, the exploration of model architectures remains a perennially trend…
Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation
Zhen Zhang, Meihan Liu, Anhui Wang +4
Unsupervised Graph Domain Adaptation (UGDA) has emerged as a practical solution to transfer knowledge from a label-rich source graph to a completely unlabelled target graph. Howeve…
Gradient-Free Score-Based Sampling Methods with Ensembles
Bryan Riel, Tobias Bischoff
Recent developments in generative modeling have utilized score-based methods coupled with stochastic differential equations to sample from complex probability distributions. Howeve…
Geometric-Facilitated Denoising Diffusion Model for 3D Molecule Generation
Can Xu, Haosen Wang, Weigang Wang +2
Denoising diffusion models have shown great potential in multiple research areas. Existing diffusion-based generative methods on de novo 3D molecule generation face two major chall…
Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation
Yue Wan, Jialu Wu, Tingjun Hou +2
Reliable molecular property prediction is essential for various scientific endeavors and industrial applications, such as drug discovery. However, the data scarcity, combined with…
Graph Neural Architecture Search with GPT-4
Haishuai Wang, Yang Gao, Xin Zheng +3
Graph Neural Architecture Search (GNAS) has shown promising results in finding the best graph neural network architecture on a given graph dataset. However, existing GNAS methods s…
Graph Neural Processes for Spatio-Temporal Extrapolation
Junfeng Hu, Yuxuan Liang, Zhencheng Fan +3
We study the task of spatio-temporal extrapolation that generates data at target locations from surrounding contexts in a graph. This task is crucial as sensors that collect data a…
Interpretable machine learning-accelerated seed treatment by nanomaterials for environmental stress alleviation
Hengjie Yu, Dan Luo, Sam F. Y. Li +4
Crops are constantly challenged by different environmental conditions. Seed treatment by nanomaterials is a cost-effective and environmentally-friendly solution for environmental s…
Discriminative Radial Domain Adaptation
Zenan Huang, Jun Wen, Siheng Chen +2
Domain adaptation methods reduce domain shift typically by learning domain-invariant features. Most existing methods are built on distribution matching, e.g., adversarial domain ad…
A Low Latency Adaptive Coding Spiking Framework for Deep Reinforcement Learning
Lang Qin, Rui Yan, Huajin Tang
In recent years, spiking neural networks (SNNs) have been used in reinforcement learning (RL) due to their low power consumption and event-driven features. However, spiking reinfor…
Twin Contrastive Learning for Online Clustering
Yunfan Li, Mouxing Yang, Dezhong Peng +3
This paper proposes to perform online clustering by conducting twin contrastive learning (TCL) at the instance and cluster level. Specifically, we find that when the data is projec…
Piecewise Linear Neural Networks and Deep Learning
Qinghua Tao, Li Li, Xiaolin Huang +3
As a powerful modelling method, PieceWise Linear Neural Networks (PWLNNs) have proven successful in various fields, most recently in deep learning. To apply PWLNN methods, both the…
Accelerating Backward Aggregation in GCN Training with Execution Path Preparing on GPUs
Shaoxian Xu, Zhiyuan Shao, Ci Yang +2
The emerging Graph Convolutional Network (GCN) has now been widely used in many domains, and it is challenging to improve the efficiencies of applications by accelerating the GCN t…
Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction
Zipeng Zhong, Jie Song, Zunlei Feng +6
Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis. A popular computational paradigm formulates…
AutoGMap: Learning to Map Large-scale Sparse Graphs on Memristive Crossbars
Bo Lyu, Shengbo Wang, Shiping Wen +4
The sparse representation of graphs has shown great potential for accelerating the computation of graph applications (e.g., Social Networks, Knowledge Graphs) on traditional comput…
SenseMag: Enabling Low-Cost Traffic Monitoring using Non-invasive Magnetic Sensing
Kafeng Wang, Haoyi Xiong, Jie Zhang +3
The operation and management of intelligent transportation systems (ITS), such as traffic monitoring, relies on real-time data aggregation of vehicular traffic information, includi…
Robust Dynamic Multi-Modal Data Fusion: A Model Uncertainty Perspective
Bin Liu
This paper is concerned with multi-modal data fusion (MMDF) under unexpected modality failures in nonlinear non-Gaussian dynamic processes. An efficient framework to tackle this pr…
Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review
Dongrui Wu, Jiaxin Xu, Weili Fang +5
Physiological computing uses human physiological data as system inputs in real time. It includes, or significantly overlaps with, brain-computer interfaces, affective computing, ad…