Viktor Zaverkin

According to our database, Viktor Zaverkin authored at least 16 papers between 2021 and 2026.

Collaborative distances:

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

2026

On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists.

Seungone Kim, Dongkeun Yoon, Kiril Gashteovski, Juyoung Suk, Jinheon Baek, Pranjal Aggarwal, Ian Wu, Viktor Zaverkin, Spase Petkoski, Daniel R. Schrider, Ilija Dukovski, Francesco Santini, Biljana Mitreska, Yong Jeong, Kyeongha Kwon, Young Min Sim, Dragana Manasova, Arthur Porto, Biljana Mojsoska, Makoto Takamoto, Marko Shuntov, Ruoqi Liu, Hyunjoo Jenny Lee, Niyazi Ulas Dinç, Yehhyun Jo, Sunkyu Han, Chungwoo Lee, Huishan Li, Esther H. R. Tsai, Ergun Simsek, Khushboo Shafi, Yeonseung Chung, Jihye Park, Aleksandar Shulevski, Henrik Christiansen, Yoosang Son, Elly Knight, Amanda Montoya, Jeongyoun Ahn, Christian Langkammer, Heera Moon, Changwon Yoon, Nikola Stikov, Mooseok Jang, Edward Choi, Junhan Kim, Yeon Sik Jung, Woo Youn Kim, Jae Kyoung Kim, Ishraq Md Anjum, Hyun Uk Kim, Drew Bridges, Carolin Lawrence, Xiang Yue, Alice Oh, Akari Asai, Sean Welleck, Graham Neubig

CoRR, May, 2026

2025

Transferable long-range interactions in machine-learned interatomic potentials: Dataset.

Viktor Zaverkin, Matheus Ferraz, Francesco Alesiani, Henrik Christiansen, Makoto Takamoto, Federico Errica, Mathias Niepert

Dataset, December, 2025

Performance of universal machine-learned potentials with explicit long-range interactions in biomolecular simulations.

Viktor Zaverkin, Matheus Ferraz, Francesco Alesiani, Mathias Niepert

CoRR, August, 2025

Performance of universal machine-learned potentials with explicit long-range interactions in biomolecular simulations: Datasets and models.

Viktor Zaverkin, Matheus Ferraz, Francesco Alesiani, Mathias Niepert

Dataset, July, 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

Adaptive Width Neural Networks.

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

CoRR, January, 2025

Physics-Informed Weakly Supervised Learning For Interatomic Potentials.

Makoto Takamoto, Viktor Zaverkin, Mathias Niepert

Proceedings of the Forty-second International Conference on Machine Learning, 2025

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

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

Proceedings of the Forty-second International Conference on Machine Learning, 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

Proceedings of the Forty-second International Conference on Machine Learning, 2025

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 37: 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

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