341 citations · 608 across the 18 of their papers we have counts for
5 papers · 1 filter
A Data-Centric Optimization Framework for Machine Learning
Oliver Rausch, Tal Ben-Nun, Nikoli Dryden +3
Rapid progress in deep learning is leading to a diverse set of quickly changing models, with a dramatically growing demand for compute. However, as frameworks specialize performanc…
Learning Combinatorial Node Labeling Algorithms
Lukas Gianinazzi, Maximilian Fries, Nikoli Dryden +3
We present a novel neural architecture to solve graph optimization problems where the solution consists of arbitrary node labels, allowing us to solve hard problems like graph colo…
Motif Prediction with Graph Neural Networks
Maciej Besta, Raphael Grob, Cesare Miglioli +8
Link prediction is one of the central problems in graph mining. However, recent studies highlight the importance of higher-order network analysis, where complex structures called m…
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks
Torsten Hoefler, Dan Alistarh, Tal Ben-Nun +2
The growing energy and performance costs of deep learning have driven the community to reduce the size of neural networks by selectively pruning components. Similarly to their biol…
Clairvoyant Prefetching for Distributed Machine Learning I/O
Nikoli Dryden, Roman Böhringer, Tal Ben-Nun +1
I/O is emerging as a major bottleneck for machine learning training, especially in distributed environments. Indeed, at large scale, I/O takes as much as 85% of training time. Addr…