Zhouchen Lin

Dissecting the Diffusion Process in Linear Graph Convolutional Networks

2021 · arXiv (Cornell University)

Graph Convolutional Networks (GCNs) have attracted more and more attentions in recent years. A typical GCN layer consists of a linear feature propagation step and a nonlinear transformation step. Recent works show that a linear …

Video Surveillance for Indoor Office Environment Based on Object-Level Anomaly Detection

2023 · Journal of Physics Conference Series

Abstract Traditional methods of Abnormal Behavior Detection (ABD) process the surveillance video on frame-level, which ignores object-level abnormal behavior patterns. To address the problem, this paper presents Object-Level Anomaly Detection model (OLAD), which aims to …

Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks

2024 · arXiv (Cornell University)

Neuromorphic computing with spiking neural networks is promising for energy-efficient artificial intelligence (AI) applications. However, different from humans who continually learn different tasks in a lifetime, neural network models suffer from catastrophic forgetting. How could …

AI Pangaea: Unifying Intelligence Islands for Adapting Myriad Tasks

2025 · arXiv (Cornell University)

The pursuit of artificial general intelligence continuously demands generalization in one model across myriad tasks, even those not seen before. However, current AI models are isolated from each other for being limited to specific tasks, …

Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

Graph Convolutional Networks (GCNs) play a crucial role in graph learning tasks, however, learning graph embedding with few supervised signals is still a difficult problem. In this paper, we propose a novel training algorithm for …