18 citations · 32 across the 21 of their papers we have counts for
11 papers · 1 filter
Rethinking Generalization in Graph Neural Networks: A Structural Complexity Perspective
Peiyao Wang, Liang Bai, Xian Yang +2
Graph neural networks (GNNs) have emerged as a fundamental tool for learning from graph-structured data, achieving strong performance across a wide range of applications. However,…
Time-varying Interaction Graph ODE for Dynamic Graph Representation Learning
Xiaoyi Wang, Zhiqiang Wang, Jianqing Liang +4
Graph neural Ordinary Differential Equations (ODE) combine neural ODE with the message passing mechanism of Graph Neural Networks (GNN), providing a continuous-time modeling method…
Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies
Yi Ma, Hongyao Tang, Chenjun Xiao +4
In recent years, the expansion of neural network models and training data has driven remarkable progress in deep learning, particularly in computer vision and natural language proc…
Positive region preserved random sampling: an efficient feature selection method for massive data
Hexiang Bai, Deyu Li, Jiye Liang +1
Selecting relevant features is an important and necessary step for intelligent machines to maximize their chances of success. However, intelligent machines generally have no enough…
A Survey of Continual Reinforcement Learning
Chaofan Pan, Xin Yang, Yanhua Li +4
Reinforcement Learning (RL) is an important machine learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in this field…
C-LoRA: Continual Low-Rank Adaptation for Pre-trained Visual Models
Xin Zhang, Liang Bai, Xian Yang +1
Pre-trained visual models have become fundamental in computer vision, but they face challenges in continual learning scenarios where data and tasks evolve over time. Low-Rank Adapt…
Towards Effective Graph Rationalization via Boosting Environment Diversity
Yujie Wang, Kui Yu, Yuhong Zhang +2
Graph Neural Networks (GNNs) perform effectively when training and testing graphs are drawn from the same distribution, but struggle to generalize well in the face of distribution…
GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive Learning
Jianqing Liang, Xinkai Wei, Min Chen +2
Graph contrastive learning (GCL) has become a hot topic in the field of graph representation learning. In contrast to traditional supervised learning relying on a large number of l…
PipeOptim: Ensuring Effective 1F1B Schedule with Optimizer-Dependent Weight Prediction
Lei Guan, Dongsheng Li, Yongle Chen +3
Asynchronous pipeline model parallelism with a "1F1B" (one forward, one backward) schedule generates little bubble overhead and always provides quite a high throughput. However, th…
Towards Privacy-Aware Causal Structure Learning in Federated Setting
Jianli Huang, Xianjie Guo, Kui Yu +2
Causal structure learning has been extensively studied and widely used in machine learning and various applications. To achieve an ideal performance, existing causal structure lear…
How Powerful are Shallow Neural Networks with Bandlimited Random Weights?
Ming Li, Sho Sonoda, Feilong Cao +2
We investigate the expressive power of depth-2 bandlimited random neural networks. A random net is a neural network where the hidden layer parameters are frozen with random assignm…