3 citations · 5 across the 6 of their papers we have counts for
3 papers · 1 filter
MAEM: Mitigating Recency Bias via a Replay-Free, Capacity-Controlled Dynamic Mixture of Experts for Class-Incremental Graph Learning
Junlin Chen, Wenhao Wang, Junchao Xu +5
Graph Neural Networks (GNNs) excel on graph-structured data but suffer catastrophic forgetting when classes arrive sequentially. In class-incremental graph learning, predictions ar…
MultiMem: Measuring and Mitigating Memorization in Multi-Modal Contrastive Learning
Wenhao Wang, Franziska Boenisch, Michael Backes +1
Memorization in machine learning models enables high performance on rare in-distribution samples by capturing their atypical patterns. However, it also causes harmful retention of…
Learn Before Represent: Bridging Generative and Contrastive Learning for Domain-Specific LLM Embeddings
Xiaoyu Liang, Yuchen Peng, Jiale Luo +3
Large Language Models (LLMs) adapted via contrastive learning excel in general representation learning but struggle in vertical domains like chemistry and law, primarily due to a l…