Ran He

Recognizing Predictive Substructures With Subgraph Information Bottleneck

2021 · IEEE Transactions on Pattern Analysis and Machine Intelligence

The emergence of Graph Convolutional Network (GCN) has greatly boosted the progress of graph learning. However, two disturbing factors, noise and redundancy in graph data, and lack of interpretation for prediction results, impede further development …

Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flows

2021 · arXiv (Cornell University)

As Graph Neural Networks (GNNs) are widely adopted in digital pathology, there is increasing attention to developing explanation models (explainers) of GNNs for improved transparency in clinical decisions. Existing explainers discover an explanatory subgraph relevant …

Few-shot Backdoor Defense Using Shapley Estimation

2021 · arXiv (Cornell University)

Deep neural networks have achieved impressive performance in a variety of tasks over the last decade, such as autonomous driving, face recognition, and medical diagnosis. However, prior works show that deep neural networks are easily …

Jailbreak Attacks and Defenses against Multimodal Generative Models: A Survey

2024 · arXiv (Cornell University)

The rapid evolution of multimodal foundation models has led to significant advancements in cross-modal understanding and generation across diverse modalities, including text, images, audio, and video. However, these models remain susceptible to jailbreak attacks, which …

Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

2025

In the quest for artificial general intelligence, Multi-modal Large Language Models (MLLMs) have emerged as a focal point in recent advancements. However, the predominant focus remains on developing their capabilities in static image understanding. The …