Viktor Zaverkin

According to our database, Viktor Zaverkin authored at least 12 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations.

Henrik Christiansen, Takashi Maruyama, Federico Errica, Viktor Zaverkin, Makoto Takamoto, Francesco Alesiani

CoRR, March, 2025

Geometric Kolmogorov-Arnold Superposition Theorem.

Francesco Alesiani, Takashi Maruyama, Henrik Christiansen, Viktor Zaverkin

CoRR, February, 2025

Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks.

Maya Bechler-Speicher, Ben Finkelshtein, Fabrizio Frasca, Luis Müller, Jan Tönshoff, Antoine Siraudin, Viktor Zaverkin, Michael M. Bronstein, Mathias Niepert, Bryan Perozzi, Mikhail Galkin, Christopher Morris

CoRR, February, 2025

Adaptive Width Neural Networks.

Federico Errica, Henrik Christiansen, Viktor Zaverkin, Mathias Niepert, Francesco Alesiani

CoRR, January, 2025

2024

Physics-Informed Weakly Supervised Learning for Interatomic Potentials.

Makoto Takamoto, Viktor Zaverkin, Mathias Niepert

CoRR, 2024

Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing.

Viktor Zaverkin, Francesco Alesiani, Takashi Maruyama, Federico Errica, Henrik Christiansen, Makoto Takamoto, Nicolas Weber, Mathias Niepert

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks.

Duy Minh Ho Nguyen, Nina Lukashina, Tai Nguyen, An T. Le, TrungTin Nguyen, Nhat Ho, Jan Peters, Daniel Sonntag, Viktor Zaverkin, Mathias Niepert

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3].

David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart

Dataset, April, 2023

A Framework and Benchmark for Deep Batch Active Learning for Regression.

David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart

J. Mach. Learn. Res., 2023

Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching.

Federico Errica, Henrik Christiansen, Viktor Zaverkin, Takashi Maruyama, Mathias Niepert, Francesco Alesiani

CoRR, 2023

2021

Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments.

Viktor Zaverkin, David Holzmüller, Ingo Steinwart, Johannes Kästner

Dataset, October, 2021

Exploration of transferable and uniformly accurate neural network interatomic potentials using optimal experimental design.

Viktor Zaverkin, Johannes Kästner

Mach. Learn. Sci. Technol., 2021