1 citations · 1 across the 10 of their papers we have counts for
11 papers
Fenchel Tilting: Weighted Correction for Efficient Finetuning of Generative Models
Maksim Bobrin, Maksim Zhdanov, Dmitry Dylov
Adapting a pretrained generative model to an arbitrary preference expressed as a utility function underlies reward alignment, guided design, and constraint satisfaction, enabling d…
Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions
David R. Wessels, Farhad Ramezanghorbani, Alireza Moradzadeh +9
Subquadratic alternatives to attention require compromises when applied to multi-dimensional data: standard convolutions lack global receptive fields and input dependency, while re…
Harmonic Torsional Diffusion for Protein-Ligand Flexible Docking
Maksim Zhdanov, Pavel Strashnov, Vladislav Kurenkov
Molecular docking requires reasoning jointly about ligand pose and protein flexibility. Most diffusion-based docking models predict torsional updates with generic Euclidean heads t…
Uncertainty-aware Machine Learning Interatomic Potentials via Learned Functional Perturbations
Olga Zaghen, Maksim Zhdanov, Dario Coscia +2
Machine Learning Interatomic Potentials (MLIPs) achieve near ab initio accuracy at a fraction of the cost of quantum-mechanical simulations, yet they remain prone to silent failure…
(Sparse) Attention to the Details: Preserving Spectral Fidelity in ML-based Weather Forecasting Models
Maksim Zhdanov, Ana Lucic, Max Welling +1
We introduce Mosaic, a probabilistic weather forecasting model that addresses three failure modes of spectral degradation in ML-based weather prediction: spectral damping (statisti…
Vintix II: Decision Pre-Trained Transformer is a Scalable In-Context Reinforcement Learner
Andrei Polubarov, Lyubaykin Nikita, Alexander Derevyagin +11
Recent progress in in-context reinforcement learning (ICRL) has demonstrated its potential for training generalist agents that can acquire new tasks directly at inference. Algorith…
MSPT: Efficient Large-Scale Physical Modeling via Parallelized Multi-Scale Attention
Pedro M. P. Curvo, Jan-Willem van de Meent, Maksim Zhdanov
A key scalability challenge in neural solvers for industrial-scale physics simulations is efficiently capturing both fine-grained local interactions and long-range global dependenc…
Adaptive Mesh-Quantization for Neural PDE Solvers
Winfried van den Dool, Maksim Zhdanov, Yuki M. Asano +1
Physical systems commonly exhibit spatially varying complexity, presenting a significant challenge for neural PDE solvers. While Graph Neural Networks can handle the irregular mesh…
BSA: Ball Sparse Attention for Large-scale Geometries
Catalin E. Brita, Hieu Nguyen, Lohithsai Yadala Chanchu +2
Self-attention scales quadratically with input size, limiting its use for large-scale physical systems. Although sparse attention mechanisms provide a viable alternative, they are…
Electrostatics from Laplacian Eigenbasis for Neural Network Interatomic Potentials
Maksim Zhdanov, Vladislav Kurenkov
In this work, we introduce Phi-Module, a universal plugin module that enforces Poisson's equation within the message-passing framework to learn electrostatic interactions in a self…
Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems
Maksim Zhdanov, Max Welling, Jan-Willem van de Meent
Large-scale physical systems defined on irregular grids pose significant scalability challenges for deep learning methods, especially in the presence of long-range interactions and…