1.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Harbin Institute of TechnologyCN82 papers
- Nanjing University of Aeronautics and AstronauticsCN59 papers
- Northwestern Polytechnical UniversityCN38 papers
- Beihang UniversityCN36 papers
- Centre National de la Recherche ScientifiqueFR27 papers
- Laboratoire Charles CoulombFR23 papers
- Institut Universitaire de FranceFR22 papers
- Université de MontpellierFR22 papers
- Chinese Academy of SciencesCN20 papers
- Shanxi UniversityCN15 papers
- Beijing Institute of TechnologyCN13 papers
- University of Chinese Academy of SciencesCN12 papers
12 papers · 1 filter
Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification
Dingkun Liu, Siyang Li, Ziwei Wang +2
A non-invasive brain-computer interface (BCI) enables direct interaction between the user and external devices, typically via electroencephalogram (EEG) signals. However, decoding…
Federated Deep Subspace Clustering
Yupei Zhang, Ruojia Feng, Yifei Wang +1
This paper introduces FDSC, a private-protected subspace clustering (SC) approach with federated learning (FC) schema. In each client, there is a deep subspace clustering network a…
Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image Classification
Matteo Bianchi, Antonio De Santis, Andrea Tocchetti +1
Transparency and explainability in image classification are essential for establishing trust in machine learning models and detecting biases and errors. State-of-the-art explainabi…
A Novel Normalized-Cut Solver with Nearest Neighbor Hierarchical Initialization
Feiping Nie, Jitao Lu, Danyang Wu +2
Normalized-Cut (N-Cut) is a famous model of spectral clustering. The traditional N-Cut solvers are two-stage: 1) calculating the continuous spectral embedding of normalized Laplaci…
Multi-Label Knowledge Distillation
Penghui Yang, Ming-Kun Xie, Chen-Chen Zong +4
Existing knowledge distillation methods typically work by imparting the knowledge of output logits or intermediate feature maps from the teacher network to the student network, whi…
Dynamic Feature-based Deep Reinforcement Learning for Flow Control of Circular Cylinder with Sparse Surface Pressure Sensing
Qiulei Wang, Lei Yan, Gang Hu +3
This study proposes a self-learning algorithm for closed-loop cylinder wake control targeting lower drag and lower lift fluctuations with the additional challenge of sparse sensor…
Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks
Jiaqiang Zhang, Senzhang Wang, Songcan Chen
Detecting abnormal nodes from attributed networks is of great importance in many real applications, such as financial fraud detection and cyber security. This task is challenging d…
Matrix Completion via Non-Convex Relaxation and Adaptive Correlation Learning
Xuelong Li, Hongyuan Zhang, Rui Zhang
The existing matrix completion methods focus on optimizing the relaxation of rank function such as nuclear norm, Schatten-p norm, etc. They usually need many iterations to converge…
Distributed Second Order Methods with Fast Rates and Compressed Communication
Rustem Islamov, Xun Qian, Peter Richtárik
We develop several new communication-efficient second-order methods for distributed optimization. Our first method, NEWTON-STAR, is a variant of Newton's method from which it inher…
Convex Subspace Clustering by Adaptive Block Diagonal Representation
Yunxia Lin, Songcan Chen
Subspace clustering is a class of extensively studied clustering methods where the spectral-type approaches are its important subclass. Its key first step is to desire learning a r…
Expand Globally, Shrink Locally: Discriminant Multi-label Learning with Missing Labels
Zhongchen Ma, Songcan Chen
In multi-label learning, the issue of missing labels brings a major challenge. Many methods attempt to recovery missing labels by exploiting low-rank structure of label matrix. How…
General solutions for nonlinear differential equations: a rule-based self-learning approach using deep reinforcement learning
Shiyin Wei, Xiaowei Jin, Hui Li
A universal rule-based self-learning approach using deep reinforcement learning (DRL) is proposed for the first time to solve nonlinear ordinary differential equations and partial…