Andrei Ivanov

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 18

Top Topics

Genetically Optimized

Dynamical Systems

Top Venues

USENIX Annual Technical Conference

Publications

Andrei Ivanov, Stefan Maria Ailuro
TMPNN: High-Order Polynomial Regression Based on Taylor Map Factorization. AAAI (2024)
Andrei Ivanov, Benjamin Rothenberger, Arnaud Dethise, Marco Canini, Torsten Hoefler, Adrian Perrig
SAGE: Software-based Attestation for GPU Execution. USENIX Annual Technical Conference (2023)
Roberto L. Castro, Andrei Ivanov, Diego Andrade, Tal Ben-Nun, Basilio B. Fraguela, Torsten Hoefler
VENOM: A Vectorized N: M Format for Unleashing the Power of Sparse Tensor Cores. SC (2023)
Andrei Ivanov, Stefan Maria Ailuro
TMPNN: High-Order Polynomial Regression Based on Taylor Map Factorization. CoRR (2023)
Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Saleh Ashkboos, Torsten Hoefler
STen: Productive and Efficient Sparsity in PyTorch. CoRR (2023)
Julia Bazinska, Andrei Ivanov, Tal Ben-Nun, Nikoli Dryden, Maciej Besta, Siyuan Shen, Torsten Hoefler
Cached Operator Reordering: A Unified View for Fast GNN Training. CoRR (2023)
Roberto L. Castro, Andrei Ivanov, Diego Andrade, Tal Ben-Nun, Basilio B. Fraguela, Torsten Hoefler
VENOM: A Vectorized N: M Format for Unleashing the Power of Sparse Tensor Cores. CoRR (2023)
Oliver Rausch, Tal Ben-Nun, Nikoli Dryden, Andrei Ivanov, Shigang Li, Torsten Hoefler
A data-centric optimization framework for machine learning. ICS (2022)
Andrei Ivanov, Benjamin Rothenberger, Arnaud Dethise, Marco Canini, Torsten Hoefler, Adrian Perrig
SAGE: Software-based Attestation for GPU Execution. CoRR (2022)
Oliver Rausch, Tal Ben-Nun, Nikoli Dryden, Andrei Ivanov, Shigang Li, Torsten Hoefler
A Data-Centric Optimization Framework for Machine Learning. CoRR (2021)
Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Shigang Li, Torsten Hoefler
Data Movement Is All You Need: A Case Study on Optimizing Transformers. MLSys (2021)
Andrei Ivanov, Uwe Iben, Anna Golovkina
Physics-based polynomial neural networks for one-shot learning of dynamical systems from one or a few samples. CoRR (2020)
Andrei Ivanov, Ilya Agapov
Physics-Based Deep Neural Networks for Beam Dynamics in Charged Particle Accelerators. CoRR (2020)
Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Shigang Li, Torsten Hoefler
Data Movement Is All You Need: A Case Study on Optimizing Transformers. CoRR (2020)
Andrei Ivanov, Anna Golovkina, Uwe Iben
Polynomial Neural Networks and Taylor Maps for Dynamical Systems Simulation and Learning. ECAI (2020)
Andrei Ivanov, Sergei Andrianov
Matrix Lie Maps and Neural Networks for Solving Differential Equations. CoRR (2019)
Andrei Ivanov, Anna Golovkina, Uwe Iben
Polynomial Neural Networks and Taylor maps for Dynamical Systems Simulation and Learning. CoRR (2019)
Andrei Ivanov, Sergei Andrianov, Alena Sholokhova
Lie Transform Based Polynomial Neural Networks for Dynamical Systems Simulation and Identification. CoRR (2018)