1 citations · 1 across the 5 of their papers we have counts for
6 papers
Hierarchical MoE for Multi-Modal ILD Diagnosis
Alec K. Peltekian, Gorkem Durak, Halil Ertugrul Aktas +9
Mixture-of-experts (MoE) models combine specialized predictors under learned routing, offering a principled mechanism for leveraging heterogeneity in medical data. We present a hie…
Multi-Resolution Model Fusion for Accelerating the Convolutional Neural Network Training
Kewei Wang, Claire Songhyun Lee, Sunwoo Lee +8
Neural networks are rapidly gaining popularity in scientific research, but training the models is often very time-consuming. Particularly when the training data samples are large h…
REN: Anatomically-Informed Mixture-of-Experts for Interstitial Lung Disease Diagnosis
Alec K. Peltekian, Halil Ertugrul Aktas, Gorkem Durak +10
Mixture-of-Experts (MoE) architectures achieve scalable learning by routing inputs to specialized subnetworks through conditional computation. However, conventional MoE designs ass…
Imaging-Based Mortality Prediction in Patients with Systemic Sclerosis
Alec K. Peltekian, Karolina Senkow, Gorkem Durak +23
Interstitial lung disease (ILD) is a leading cause of morbidity and mortality in systemic sclerosis (SSc). Chest computed tomography (CT) is the primary imaging modality for diagno…
Parallel Data Object Creation: Towards Scalable Metadata Management in High-Performance I/O Library
Youjia Li, Robert Latham, Robert Ross +3
High-level I/O libraries, such as HDF5 and PnetCDF, are commonly used by large-scale scientific applications to perform I/O tasks in parallel. These I/O libraries store the metadat…
Towards Space Group Determination from EBSD Patterns: The Role of Deep Learning and High-throughput Dynamical Simulations
Alfred Yan, Muhammad Nur Talha Kilic, Gert Nolze +4
The design of novel materials hinges on the understanding of structure-property relationships. However, in recent times, our capability to synthesize a large number of materials ha…