351 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Zhejiang UniversityCN124 papers
- Fudan UniversityCN73 papers
- Tsinghua UniversityCN46 papers
- Chinese Academy of SciencesCN45 papers
- University of Chinese Academy of SciencesCN39 papers
- Nanjing UniversityCN31 papers
- Peking UniversityCN26 papers
- University of Science and Technology of ChinaCN26 papers
- Vladimir State UniversityRU26 papers
- St Petersburg UniversityRU25 papers
- Collaborative Innovation Center of Advanced MicrostructuresCN24 papers
- Beihang UniversityCN22 papers
18 papers · 1 filter
Efficient and Scalable Multi-View Masked Autoencoder for Heterogeneous Graph Self-Supervised Learning
Hao Geng, Deqing Wang, Lihao Ge +5
Recent years have witnessed the flourishing of self-supervised learning on heterogeneous graphs, e.g., masked autoencoder-based methods. However, existing methods suffer from three…
A Sliced-Wasserstein Framework on Correlation Matrices for EEG Decoding
Chen Hu, Rui Wang, Jiale Zhou +4
Electroencephalography (EEG) offers noninvasive, millisecond resolution recordings of neuronal activity and is widely used in neuroscience and healthcare. Many EEG decoding pipelin…
MemNovo: Look Back at the Spectrum for Balanced De Novo Peptide Sequencing from Mass Spectrometry
Dongxin Lyu, Jingbo Zhou, Hongxin Xiang +2
De novo peptide sequencing from tandem mass spectrometry is pivotal in proteomics, enabling identification of novel peptides without reference databases. While recent Transformer-b…
ViroBench: Benchmarking Nucleotide Foundation Models on Viral Genomics Tasks
Dongxin Ye, Fang Hu, Han Hu +6
Nucleotide sequences constitute the fundamental genetic basis of biological systems, rendering viral genomic analysis critical for biomedical advancement. Despite progress in biolo…
Multimodal Functional Maximum Correlation for Emotion Recognition
Deyang Zheng, Tianyi Zhang, Wenming Zheng +1
Emotional states manifest as coordinated yet heterogeneous physiological responses across central and autonomic systems, posing a fundamental challenge for multimodal representatio…
Federated Loss Exploration for Improved Convergence on Non-IID Data
Christian Internò, Markus Olhofer, Yaochu Jin +1
Federated learning (FL) has emerged as a groundbreaking paradigm in machine learning (ML), offering privacy-preserving collaborative model training across diverse datasets. Despite…
MoE-Enhanced Explainable Deep Manifold Transformation for Complex Data Embedding and Visualization
Zelin Zang, Yuhao Wang, Jinlin Wu +4
Dimensionality reduction (DR) plays a crucial role in various fields, including data engineering and visualization, by simplifying complex datasets while retaining essential inform…
Pruning Federated Models through Loss Landscape Analysis and Client Agreement Scoring
Christian Internò, Elena Raponi, Markus Olhofer +5
The practical deployment of Federated Learning (FL) on resource-constrained devices is fundamentally limited by the high cost of training large models and the instability caused by…
Learning on Graphs with Out-of-Distribution Nodes
Yu Song, Donglin Wang
Graph Neural Networks (GNNs) are state-of-the-art models for performing prediction tasks on graphs. While existing GNNs have shown great performance on various tasks related to gra…
OER: Offline Experience Replay for Continual Offline Reinforcement Learning
Sibo Gai, Donglin Wang, Li He
The capability of continuously learning new skills via a sequence of pre-collected offline datasets is desired for an agent. However, consecutively learning a sequence of offline t…
Physics-Informed Bayesian Learning of Electrohydrodynamic Polymer Jet Printing Dynamics
Athanasios Oikonomou, Theodoros Loutas, Dixia Fan +4
Calibration of highly dynamic multi-physics manufacturing processes such as electro-hydrodynamics-based additive manufacturing (AM) technologies (E-jet printing) is still performed…
Bridging the Gap Between Patient-specific and Patient-independent Seizure Prediction via Knowledge Distillation
Di Wu, Jie Yang, Mohamad Sawan
Objective. Deep neural networks (DNNs) have shown unprecedented success in various brain-machine interface applications such as epileptic seizure prediction. However, existing appr…
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation
Jun Xia, Lirong Wu, Jintao Chen +2
Graph contrastive learning (GCL) has emerged as a dominant technique for graph representation learning which maximizes the mutual information between paired graph augmentations tha…
Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning
Jinxin Liu, Hao Shen, Donglin Wang +2
Unsupervised reinforcement learning aims to acquire skills without prior goal representations, where an agent automatically explores an open-ended environment to represent goals an…
Co-learning: Learning from Noisy Labels with Self-supervision
Cheng Tan, Jun Xia, Lirong Wu +1
Noisy labels, resulting from mistakes in manual labeling or webly data collecting for supervised learning, can cause neural networks to overfit the misleading information and degra…
A Novel Multi-scale Dilated 3D CNN for Epileptic Seizure Prediction
Ziyu Wang, Jie Yang, Mohamad Sawan
Accurate prediction of epileptic seizures allows patients to take preventive measures in advance to avoid possible injuries. In this work, a novel convolutional neural network (CNN…
Do Transformer Modifications Transfer Across Implementations and Applications?
Sharan Narang, Hyung Won Chung, Yi Tay +13
The research community has proposed copious modifications to the Transformer architecture since it was introduced over three years ago, relatively few of which have seen widespread…
The Adversarial Attack and Detection under the Fisher Information Metric
Chenxiao Zhao, P. Thomas Fletcher, Mixue Yu +3
Many deep learning models are vulnerable to the adversarial attack, i.e., imperceptible but intentionally-designed perturbations to the input can cause incorrect output of the netw…