Distilled Pruning: Using Synthetic Data to Win the Lottery. - Luke McDermott; Daniel Cummings (CoRR, 2023)

A Generalization of Continuous Relaxation in Structured Pruning. - Brad Larson; Bishal Upadhyaya; Luke McDermott; Siddha Ganju (CoRR, 2023)

GraFT: Gradual Fusion Transformer for Multimodal Re-Identification. - Haoli Yin; Jiayao Li; Eva Schiller; Luke McDermott; Daniel Cummings (CoRR, 2023)

Linear Mode Connectivity in Sparse Neural Networks. - Luke McDermott; Daniel Cummings (CoRR, 2023)

UniCat: Crafting a Stronger Fusion Baseline for Multimodal Re-Identification. - Jennifer Crawford; Haoli Yin; Luke McDermott; Daniel Cummings (CoRR, 2023)

Neural Architecture Codesign for Fast Bragg Peak Analysis. - Luke McDermott; Jason Weitz; Dmitri Demler; Daniel Cummings; Nhan Tran; Javier M. Duarte (CoRR, 2023)

Embedding Compression for Efficient Re-Identification. - Luke McDermott (CoRR, 2024)

Neural Architecture Codesign for Fast Physics Applications. - Jason Weitz; Dmitri Demler; Luke McDermott; Nhan Tran; Javier M. Duarte (CoRR, 2025)

Finding Stable Subnetworks at Initialization with Dataset Distillation. - Luke McDermott; Rahul Parhi (CoRR, 2025)

LoLA: Low-Rank Linear Attention With Sparse Caching. - Luke McDermott; Robert W. Heath Jr.; Rahul Parhi (CoRR, 2025)