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08/23/2023
Cached Operator Reordering: A Unified View for Fast GNN Training
Graph Neural Networks (GNNs) are a powerful tool for handling structured...
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04/15/2023
STen: Productive and Efficient Sparsity in PyTorch
As deep learning models grow, sparsity is becoming an increasingly criti...
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11/24/2022
Spatial Mixture-of-Experts
Many data have an underlying dependence on spatial location; it may be w...
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09/20/2022
Neural Graph Databases
Graph databases (GDBs) enable processing and analysis of unstructured, c...
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06/29/2022
ENS-10: A Dataset For Post-Processing Ensemble Weather Forecast
Post-processing ensemble prediction systems can improve weather forecast...
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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...
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06/07/2021
Learning Combinatorial Node Labeling Algorithms
We present a graph neural network to learn graph coloring heuristics usi...
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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...
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01/21/2021
Clairvoyant Prefetching for Distributed Machine Learning I/O
I/O is emerging as a major bottleneck for machine learning training, esp...
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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 ...
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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 ...
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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...
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05/18/2020
Deep Learning for Post-Processing Ensemble Weather Forecasts
Quantifying uncertainty in weather forecasts typically employs ensemble ...
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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 ...
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11/02/2019
Predicting Weather Uncertainty with Deep Convnets
Modern weather forecast models perform uncertainty quantification using ...
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03/15/2019