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Found 12 papers, 5 papers with code
VENOM: A Vectorized N:M Format for Unleashing the Power of Sparse Tensor Cores
We present the V:N:M format, which enables the execution of arbitrary N:M ratios on SPTCs.
Cached Operator Reordering: A Unified View for Fast GNN Training
Graph Neural Networks (GNNs) are a powerful tool for handling structured graph data and addressing tasks such as node classification, graph classification, and clustering.
TMPNN: High-Order Polynomial Regression Based on Taylor Map Factorization
Polynomial regression is widely used and can help to express nonlinear patterns.
STen: Productive and Efficient Sparsity in PyTorch
As deep learning models grow, sparsity is becoming an increasingly critical component of deep neural networks, enabling improved performance and reduced storage.
A Data-Centric Optimization Framework for Machine Learning
Rapid progress in deep learning is leading to a diverse set of quickly changing models, with a dramatically growing demand for compute.
Anomaly detection in dynamical systems from measured time series
The paper addresses a problem of abnormalities detection in nonlinear processes represented by measured time series.
Physics-Based Deep Neural Networks for Beam Dynamics in Charged Particle Accelerators
This paper presents a novel approach for constructing neural networks which model charged particle beam dynamics.
Data Movement Is All You Need: A Case Study on Optimizing Transformers
Transformers are one of the most important machine learning workloads today.
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 knowledge into the neural network to improve data efficiency and the generalization of predictive models.
Polynomial Neural Networks and Taylor maps for Dynamical Systems Simulation and Learning
The connection of Taylor maps and polynomial neural networks (PNN) to solve ordinary differential equations (ODEs) numerically is considered.
Matrix Lie Maps and Neural Networks for Solving Differential Equations
The weights of the network can be directly calculated from the equation.
Lie Transform--based Neural Networks for Dynamics Simulation and Learning
We also demonstrate an interpretation of the fitted neural network by converting it to a system of differential equations.
