2 citations · 3 across the 5 of their papers we have counts for
4 papers · 1 filter
Algorithmic Simplification of Neural Networks with Mosaic-of-Motifs
Pedram Bakhtiarifard, Tong Chen, Jonathan Wenshøj +2
Large-scale deep learning models are well-suited for compression. Across a variety of tasks, methods like pruning, quantization, and knowledge distillation have been used to achiev…
deCIFer: Crystal Structure Prediction from Powder Diffraction Data using Autoregressive Language Models
Frederik Lizak Johansen, Ulrik Friis-Jensen, Erik Bjørnager Dam +3
Novel materials drive advancements in fields ranging from energy storage to electronics, with crystal structure characterization forming a crucial yet challenging step in materials…
PePR: Performance Per Resource Unit as a Metric to Promote Small-Scale Deep Learning in Medical Image Analysis
Raghavendra Selvan, Bob Pepin, Christian Igel +2
The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute. These large-scale resources have been used to train progressively larger…
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning
Ulrik Friis-Jensen, Frederik L. Johansen, Andy S. Anker +3
Advances in graph machine learning (ML) have been driven by applications in chemistry as graphs have remained the most expressive representations of molecules. While early graph ML…