CPS Computational Learning Laboratory
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Journal Club
Presentation 16: Junnan He - 2025.04.14
Description: Relationship between Feature Extraction Capability and Model Structure for Time Series Data.
Presentation 15: Jinyi Liu - 2025.03.31
Description: Improve Sample Efficiency in Multi-agent Reinforcement Learning by Exploration and state representation.
Presentation 14: Ruichong Ma - 2025.03.17
Description: Job Shop Scheduling based on Hierarchical/Multi-agent Reinforcement Learning.
Presentation 13: Jiarong Du - 2025.02.28
Description: Optimization Methods for Distributed Scheduling Problems.
Presentation 12: Man Li - 2025.02.13
Description: Physics-Informed Neural Networks (PINNs) Capable of Learning from Noisy Data.
Presentation 11: Qinghe Dong - 2025.01.20
Description: Distributed Multi-agent Reinforcement Learning.
Presentation 10: Zhicheng Chen - 2025.01.09
Description: Gradient-based Acceleration for Federated Learning Convergence.
Presentation 9: Jinghao Liu - 2024.12.25
Description: Design of Distributed Filter Based on Linear Matrix Inequality.
Presentation 8: Junnan He - 2024.12.13
Description: Multi-view Representation Learning Based on Information Bottleneck.
Presentation 7: Ruichong Ma - 2024.11.28
Description: Dynamic Multi Objective Job Shop Scheduling Based on Reinforcement Learning.
Presentation 6: Jinyi Liu - 2024.11.14
Description: Improve Sample Efficiency in Multi-agent Reinforcement Learning by Exploration.
Presentation 5: Mohan Niu - 2024.10.31
Description: Efficient Representation of Multi-Agent State Spaces.
Presentation 4: Junzhu Duan - 2024.10.17
Description: Object Detection Based on Multi-source Domain Adaptation.
Presentation 3: Xuqiang Chen - 2024.10.04
Description: Modeling in Heterogeneous Federated Environments: Overviews and Methods.
Presentation 2: Junnuo Lin - 2024.09.20
Description: Modeling in Federated Graph Neural Networks: Overview and Techniques.
Presentation 1: Jiarong Du - 2024.09.05
Description: Solving Flexible Job Shop Scheduling Problem (FJSP) Based on Graph Neural Network and Deep Reinforcement Learning.