4 papers
AL-GNN: Privacy-Preserving and Replay-Free Continual Graph Learning via Analytic Learning
Xuling Zhang, Jindong Li, Yifei Zhang +2
Continual graph learning (CGL) aims to enable graph neural networks to incrementally learn from a stream of graph structured data without forgetting previously acquired knowledge.…
From Measurement to Mitigation: Quantifying and Reducing Identity Leakage in Image Representation Encoders with Linear Subspace Removal
Daniel George, Charles Yeh, Daniel Lee +1
Frozen visual embeddings (e.g., CLIP, DINOv2/v3, SSCD) power retrieval and integrity systems, yet their use on face-containing data is constrained by unmeasured identity leakage an…
Low-Rank Adaptation for Foundation Models: A Comprehensive Review
Menglin Yang, Jialin Chen, Jinkai Tao +9
The rapid advancement of foundation modelslarge-scale neural networks trained on diverse, extensive datasetshas revolutionized artificial intelligence, enabling unprecedented advan…
Towards Non-Euclidean Foundation Models: Advancing AI Beyond Euclidean Frameworks
Menglin Yang, Yifei Zhang, Jialin Chen +2
In the era of foundation models and Large Language Models (LLMs), Euclidean space is the de facto geometric setting of our machine learning architectures. However, recent literatur…