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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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07/30/2023
TMPNN: High-Order Polynomial Regression Based on Taylor Map Factorization
Polynomial regression is widely used and can help to express nonlinear p...
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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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09/07/2022
SAGE: Software-based Attestation for GPU Execution
With the application of machine learning to security-critical and sensit...
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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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07/07/2020
Physics-Based Deep Neural Networks for Beam Dynamics in Charged Particle Accelerators
This paper presents a novel approach for constructing neural networks wh...
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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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05/24/2020
Physics-based polynomial neural networks for one-shot learning of dynamical systems from one or a few samples
This paper discusses an approach for incorporating prior physical knowle...
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05/24/2020
Physics-based polynomial neural networks for one-short learning of dynamical systems from one or a few samples
This paper discusses an approach for incorporating prior physical knowle...
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12/19/2019
Polynomial Neural Networks and Taylor maps for Dynamical Systems Simulation and Learning
The connection of Taylor maps and polynomial neural networks (PNN) to so...
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08/16/2019
Matrix Lie Maps and Neural Networks for Solving Differential Equations
The coincidence between polynomial neural networks and matrix Lie maps i...
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02/05/2018
