16 citations · 43 across the 10 of their papers we have counts for
8 papers · 1 filter
Learning System Dynamics without Forgetting
Xikun Zhang, Dongjin Song, Yushan Jiang +2
Observation-based trajectory prediction for systems with unknown dynamics is essential in fields such as physics and biology. Most existing approaches are limited to learning withi…
Topology-aware Embedding Memory for Continual Learning on Expanding Networks
Xikun Zhang, Dongjin Song, Yixin Chen +1
Memory replay based techniques have shown great success for continual learning with incrementally accumulated Euclidean data. Directly applying them to continually expanding networ…
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network
Lecheng Kong, Jiarui Feng, Hao Liu +3
While Graph Neural Networks (GNNs) recently became powerful tools in graph learning tasks, considerable efforts have been spent on improving GNNs' structural encoding ability. A pa…
Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages
Guozheng Ma, Lu Li, Sen Zhang +6
Plasticity, the ability of a neural network to evolve with new data, is crucial for high-performance and sample-efficient visual reinforcement learning (VRL). Although methods like…
Parameter Efficient Multi-task Model Fusion with Partial Linearization
Anke Tang, Li Shen, Yong Luo +5
Large pre-trained models have enabled significant advances in machine learning and served as foundation components. Model fusion methods, such as task arithmetic, have been proven…
One for All: Towards Training One Graph Model for All Classification Tasks
Hao Liu, Jiarui Feng, Lecheng Kong +4
Designing a single model to address multiple tasks has been a long-standing objective in artificial intelligence. Recently, large language models have demonstrated exceptional capa…