6 papers
Probabilistic Residual Learning for Online Recommendations
Wenyuan Wang, Yusong Zhao, Zihao Xu +11
Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items. As a result, these systems often suffe…
Compressing Sequences in the Latent Embedding Space: -Token Merging for Large Language Models
Zihao Xu, John Harvill, Ziwei Fan +3
Large Language Models (LLMs) incur significant computational and memory costs when processing long prompts, as full self-attention scales quadratically with input length. Token com…
GenVP: Generating Visual Puzzles with Contrastive Hierarchical VAEs
Kalliopi Basioti, Pritish Sahu, Qingze Tony Liu +3
Raven's Progressive Matrices (RPMs) is an established benchmark to examine the ability to perform high-level abstract visual reasoning (AVR). Despite the current success of algorit…
Implicit In-context Learning
Zhuowei Li, Zihao Xu, Ligong Han +5
In-context Learning (ICL) empowers large language models (LLMs) to swiftly adapt to unseen tasks at inference-time by prefixing a few demonstration examples before queries. Despite…
Rate-My-LoRA: Efficient and Adaptive Federated Model Tuning for Cardiac MRI Segmentation
Xiaoxiao He, Haizhou Shi, Ligong Han +7
Cardiovascular disease (CVD) and cardiac dyssynchrony are major public health problems in the United States. Precise cardiac image segmentation is crucial for extracting quantitati…
Continual Learning of Large Language Models: A Comprehensive Survey
Haizhou Shi, Zihao Xu, Hengyi Wang +6
The recent success of large language models (LLMs) trained on static, pre-collected, general datasets has sparked numerous research directions and applications. One such direction…