73 citations · 331 across the 88 of their papers we have counts for
107 papers
Zero-Shot Respiratory Sound Classification through LLM-Augmented Audio-Text Alignment
Mustafa Talha İlerisoy, Hung Manh Pham, Mathias Funk +2
Self-supervised respiratory encoders lack semantic grounding in clinical domain needed for zero-shot inference, limiting their utility without task-specific labeled data. We propos…
Fairness-Aware Network Embeddings: Methods, Applications, and Challenges
Ella Has, Harshith Kumar Yadav, Gaurav Dixit +2
Network embedding methods learn low-dimensional representations of graph-structured data to support downstream tasks such as node classification, link prediction, and influence max…
Adaptive Group-Based Counterfactual Explanations for Time-Series Rehabilitation Data
Emmanuel C. Chukwu, Rianne M. Schouten, Monique Tabak +1
Counterfactual explanations (CEs) for multivariate time-series classifiers are often difficult to interpret in domains where experts reason in terms of semantic feature groups rath…
Self-evolving LLM agents with in-distribution Optimization
Yudi Zhang, Meng Fang, Zhenfang Chen +1
Large Language Models (LLMs) have recently emerged as powerful controllers for interactive agents in complex environments, yet training them to perform reliable long-horizon decisi…
When Data Is Scarce: Scaling Sparse Language Models with Repeated Training
Boqian Wu, Qiao Xiao, Patrik Okanovic +6
Scaling laws for dense LLMs under infinite data are well explored, but how sparsity interacts with limited data is not. In this work, we study sparse training in data-constrained r…
Memory-Efficient LLM Training with Dynamic Sparsity: From Stability to Practical Scaling
Qiao Xiao, Boqian Wu, Patrik Okanovic +6
Dynamic Sparse Training (DST) offers a promising paradigm for improving the training and inference efficiency of deep neural networks; however, we find that in large language model…