8 papers
Learning Graph Energies for Finite-Time Generation
Amartya Banerjee, Xingyu Xu, Caroline Moosmüller +1
Energy-based models can represent distributions over discrete structures such as molecular graphs, but an energy that is accurate at equilibrium need not yield accurate samples whe…
BiTimeCrossNet: Time-Aware Self-Supervised Learning for Pediatric Sleep
Saurav Raj Pandey, Harlin Lee
We present BiTimeCrossNet (BTCNet), a multimodal self-supervised learning framework for long physiological recordings such as overnight sleep studies. While many existing approache…
Scaling Unsupervised Multi-Source Federated Domain Adaptation through Group-Wise Discrepancy Minimization
Larissa Reichart, Cem Ata Baykara, Ali Burak Ünal +2
Unsupervised multi-source domain adaptation (UMDA) leverages labeled data from multiple source domains to generalize to an unlabeled target. While federated UMDA addresses privacy…
A Closeness Centrality-based Circuit Partitioner for Quantum Simulations
Doru Thom Popovici, Harlin Lee, Mauro Del Ben +5
Simulating quantum circuits (QC) on high-performance computing (HPC) systems has become an essential method to benchmark algorithms and probe the potential of large-scale quantum c…
RELATE: Relation Extraction in Biomedical Abstracts with LLMs and Ontology Constraints
Olawumi Olasunkanmi, Mathew Satusky, Hong Yi +3
Biomedical knowledge graphs (KGs) are vital for drug discovery and clinical decision support but remain incomplete. Large language models (LLMs) excel at extracting biomedical rela…
Federated Learning for Epileptic Seizure Prediction Across Heterogeneous EEG Datasets
Cem Ata Baykara, Saurav Raj Pandey, Ali Burak Ünal +2
Developing accurate and generalizable epileptic seizure prediction models from electroencephalography (EEG) data across multiple clinical sites is hindered by patient privacy regul…
Adaptive Multimodal Protein Plug-and-Play with Diffusion-Based Priors
Amartya Banerjee, Xingyu Xu, Caroline Moosmüller +1
In an inverse problem, the goal is to recover an unknown parameter (e.g., an image) that has typically undergone some lossy or noisy transformation during measurement. Recently, de…
Empirical Power Analysis of a Statistical Test to Quantify Gerrymandering
Ranthony A. Clark, Susan Glenn, Harlin Lee +1
Gerrymandering is a pervasive problem within the US political system. In the past decade, methods based on Markov Chain Monte Carlo (MCMC) sampling and statistical outlier tests ha…