4 papers
Ensembles of Low-Rank Expert Adapters
Yinghao Li, Vianne Gao, Chao Zhang +1
The training and fine-tuning of large language models (LLMs) often involve diverse textual data from multiple sources, which poses challenges due to conflicting gradient directions…
TGTOD: A Global Temporal Graph Transformer for Outlier Detection at Scale
Kay Liu, Jiahao Ding, MohamadAli Torkamani +1
While Transformers have revolutionized machine learning on various data, existing Transformers for temporal graphs face limitations in (1) restricted receptive fields, (2) overhead…
Robustness Reprogramming for Representation Learning
Zhichao Hou, MohamadAli Torkamani, Hamid Krim +1
This work tackles an intriguing and fundamental open challenge in representation learning: Given a well-trained deep learning model, can it be reprogrammed to enhance its robustnes…
HLogformer: A Hierarchical Transformer for Representing Log Data
Zhichao Hou, Mina Ghashami, Mikhail Kuznetsov +1
Transformers have gained widespread acclaim for their versatility in handling diverse data structures, yet their application to log data remains underexplored. Log data, characteri…