Nikoli Dryden | DeepAI

DeepAI AI Chat

Featured Co-authors

Kate Saenko
150 publications
Torsten Hoefler
137 publications
Dan Alistarh
79 publications
Maciej Besta
46 publications
Tal Ben-Nun
42 publications
Bryan A. Plummer
39 publications
Satoshi Matsuoka
26 publications
Salvatore Di Girolamo
17 publications
Shigang Li
16 publications
Lukas Gianinazzi
15 publications
Andrei Ivanov
12 publications

∙ 08/23/2023

Cached Operator Reordering: A Unified View for Fast GNN Training

Graph Neural Networks (GNNs) are a powerful tool for handling structured...

0 Julia Bazinska, et al. ∙

∙ 04/15/2023

STen: Productive and Efficient Sparsity in PyTorch

As deep learning models grow, sparsity is becoming an increasingly criti...

0 Andrei Ivanov, et al. ∙

∙ 11/24/2022

Spatial Mixture-of-Experts

Many data have an underlying dependence on spatial location; it may be w...

0 Nikoli Dryden, et al. ∙

∙ 09/20/2022

Neural Graph Databases

Graph databases (GDBs) enable processing and analysis of unstructured, c...

65 Maciej Besta, et al. ∙

∙ 06/29/2022

ENS-10: A Dataset For Post-Processing Ensemble Weather Forecast

Post-processing ensemble prediction systems can improve weather forecast...

14 Saleh Ashkboos, et al. ∙

∙ 10/20/2021

A Data-Centric Optimization Framework for Machine Learning

Rapid progress in deep learning is leading to a diverse set of quickly c...

19 Oliver Rausch, et al. ∙

∙ 06/07/2021

Learning Combinatorial Node Labeling Algorithms

We present a graph neural network to learn graph coloring heuristics usi...

6 Lukas Gianinazzi, et al. ∙

∙ 01/31/2021

Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks

The growing energy and performance costs of deep learning have driven th...

85 Torsten Hoefler, et al. ∙

∙ 01/21/2021

Clairvoyant Prefetching for Distributed Machine Learning I/O

I/O is emerging as a major bottleneck for machine learning training, esp...

28 Roman Böhringer, et al. ∙

∙ 07/25/2020

The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism

We present scalable hybrid-parallel algorithms for training large-scale ...

0 Yosuke Oyama, et al. ∙

∙ 06/30/2020

Data Movement Is All You Need: A Case Study on Optimizing Transformers

Transformers have become widely used for language modeling and sequence ...

45 Andrei Ivanov, et al. ∙

∙ 06/18/2020

Shapeshifter Networks: Cross-layer Parameter Sharing for Scalable and Effective Deep Learning

We present Shapeshifter Networks (SSNs), a flexible neural network frame...

0 Bryan A. Plummer, et al. ∙

∙ 05/18/2020

Deep Learning for Post-Processing Ensemble Weather Forecasts

Quantifying uncertainty in weather forecasts typically employs ensemble ...

42 Peter Grönquist, et al. ∙

∙ 04/30/2020

Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging

Deep learning at scale is dominated by communication time. Distributing ...

1 Shigang Li, et al. ∙

∙ 11/02/2019

Predicting Weather Uncertainty with Deep Convnets

Modern weather forecast models perform uncertainty quantification using ...

1 Peter Grönquist, et al. ∙

∙ 03/15/2019

Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism

Scaling CNN training is necessary to keep up with growing datasets and r...

0 Nikoli Dryden, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world