6 papers
SimAuthor: Harnessing Foundation Models for Persistent Scientific Simulator Authoring
Yishan Wang, Ran Piao, Mathias Funk +1
Foundation models can generate scientific code, but authoring a scientific simulator (an executable program encoding hypotheses about how mechanisms generate observable signals) re…
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…
StethoLM: Audio Language Model for Cardiopulmonary Analysis Across Clinical Tasks
Yishan Wang, Tsai-Ning Wang, Mathias Funk +1
Listening to heart and lung sounds - auscultation - is one of the first and most fundamental steps in a clinical examination. Despite being fast and non-invasive, it demands years…
Helmsman: Autonomous Synthesis of Federated Learning Systems via Collaborative LLM Agents
Haoyuan Li, Mathias Funk, Aaqib Saeed
Federated Learning (FL) offers a powerful paradigm for training models on decentralized data, but its promise is often undermined by the immense complexity of designing and deployi…
FAST: Federated Active Learning with Foundation Models for Communication-efficient Sampling and Training
Haoyuan Li, Mathias Funk, Jindong Wang +1
Federated Active Learning (FAL) has emerged as a promising framework to leverage large quantities of unlabeled data across distributed clients while preserving data privacy. Howeve…
Collaboratively Learning Federated Models from Noisy Decentralized Data
Haoyuan Li, Mathias Funk, Nezihe Merve Gürel +1
Federated learning (FL) has emerged as a prominent method for collaboratively training machine learning models using local data from edge devices, all while keeping data decentrali…