8 papers
PECKER: A Precisely Efficient Critical Knowledge Erasure Recipe For Machine Unlearning in Diffusion Models
Zhiyong Ma, Zhitao Deng, Huan Tang +4
Machine unlearning (MU) has become a critical technique for GenAI models' safe and compliant operation. While existing MU methods are effective, most impose prohibitive training ti…
Towards A Universal Graph Structural Encoder
Jialin Chen, Haolan Zuo, Haoyu Peter Wang +3
Recent advancements in large-scale pre-training have shown the potential to learn generalizable representations for downstream tasks. In the graph domain, however, capturing and tr…
MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering
Jialin Chen, Aosong Feng, Ziyu Zhao +7
Understanding the relationship between textual news and time-series evolution is a critical yet under-explored challenge in applied data science. While multimodal learning has gain…
TRACE: Grounding Time Series in Context for Multimodal Embedding and Retrieval
Jialin Chen, Ziyu Zhao, Gaukhar Nurbek +5
The ubiquity of dynamic data in domains such as weather, healthcare, and energy underscores a growing need for effective interpretation and retrieval of time-series data. These dat…
LitFM: A Retrieval Augmented Structure-aware Foundation Model For Citation Graphs
Jiasheng Zhang, Jialin Chen, Ali Maatouk +6
With the advent of large language models (LLMs), managing scientific literature via LLMs has become a promising direction of research. However, existing approaches often overlook t…
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…