Lie Transform Based Polynomial Neural Networks for Dynamical Systems Simulation and Identification. - Andrei Ivanov; Sergei Andrianov; Alena Sholokhova (CoRR, 2018)

Matrix Lie Maps and Neural Networks for Solving Differential Equations. - Andrei Ivanov; Sergei Andrianov (CoRR, 2019)

Polynomial Neural Networks and Taylor Maps for Dynamical Systems Simulation and Learning. - Andrei Ivanov; Anna Golovkina; Uwe Iben (ECAI, 2020)

Physics-based polynomial neural networks for one-shot learning of dynamical systems from one or a few samples. - Andrei Ivanov; Uwe Iben; Anna Golovkina (CoRR, 2020)

Physics-Based Deep Neural Networks for Beam Dynamics in Charged Particle Accelerators. - Andrei Ivanov; Ilya Agapov (CoRR, 2020)

Data Movement Is All You Need: A Case Study on Optimizing Transformers. - Andrei Ivanov; Nikoli Dryden; Tal Ben-Nun; Shigang Li 002; Torsten Hoefler (MLSys, 2021)

A data-centric optimization framework for machine learning. - Oliver Rausch; Tal Ben-Nun; Nikoli Dryden; Andrei Ivanov; Shigang Li 002; Torsten Hoefler (ICS, 2022)

VENOM: A Vectorized N: M Format for Unleashing the Power of Sparse Tensor Cores. - Roberto L. Castro; Andrei Ivanov; Diego Andrade; Tal Ben-Nun; Basilio B. Fraguela; Torsten Hoefler (SC, 2023)

SAGE: Software-based Attestation for GPU Execution. - Andrei Ivanov; Benjamin Rothenberger; Arnaud Dethise; Marco Canini; Torsten Hoefler; Adrian Perrig (USENIX ATC, 2023)

STen: Productive and Efficient Sparsity in PyTorch. - Andrei Ivanov; Nikoli Dryden; Tal Ben-Nun; Saleh Ashkboos; Torsten Hoefler (CoRR, 2023)

Cached Operator Reordering: A Unified View for Fast GNN Training. - Julia Bazinska; Andrei Ivanov; Tal Ben-Nun; Nikoli Dryden; Maciej Besta; Siyuan Shen; Torsten Hoefler (CoRR, 2023)

TMPNN: High-Order Polynomial Regression Based on Taylor Map Factorization. - Andrei Ivanov; Stefan Maria Ailuro (AAAI, 2024)