9 citations · 34 across the 14 of their papers we have counts for
15 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…
GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models
Jialin Chen, Houyu Zhang, Seongjun Yun +6
Retrieval-Augmented Generation (RAG) has significantly mitigated the hallucinations of Large Language Models (LLMs) by grounding the generation with external knowledge. Recent exte…
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…
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…
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…
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…
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…
DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs
Jiasheng Zhang, Jialin Chen, Menglin Yang +4
Dynamic text-attributed graphs (DyTAGs) are prevalent in various real-world scenarios, where each node and edge are associated with text descriptions, and both the graph structure…
From Similarity to Superiority: Channel Clustering for Time Series Forecasting
Jialin Chen, Jan Eric Lenssen, Aosong Feng +5
Time series forecasting has attracted significant attention in recent decades. Previous studies have demonstrated that the Channel-Independent (CI) strategy improves forecasting pe…
Efficient High-Resolution Time Series Classification via Attention Kronecker Decomposition
Aosong Feng, Jialin Chen, Juan Garza +5
The high-resolution time series classification problem is essential due to the increasing availability of detailed temporal data in various domains. To tackle this challenge effect…
Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation
Jialin Chen, Yuelin Wang, Cristian Bodnar +3
Graph convolutions have been a pivotal element in learning graph representations. However, recursively aggregating neighboring information with graph convolutions leads to indistin…
Generative Explanations for Graph Neural Network: Methods and Evaluations
Jialin Chen, Kenza Amara, Junchi Yu +1
Graph Neural Networks (GNNs) achieve state-of-the-art performance in various graph-related tasks. However, the black-box nature often limits their interpretability and trustworthin…
TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery
Jialin Chen, Rex Ying
Temporal graphs are widely used to model dynamic systems with time-varying interactions. In real-world scenarios, the underlying mechanisms of generating future interactions in dyn…
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion
Jialin Chen, Shirley Wu, Abhijit Gupta +1
The widespread deployment of Graph Neural Networks (GNNs) sparks significant interest in their explainability, which plays a vital role in model auditing and ensuring trustworthy g…