3 citations · 3 across the 4 of their papers we have counts for
5 papers
Compressed magnetic Moment Tensor Potentials via low-rank matrix and tensor factorizations
Alexey S. Kotykhov, Alexander V. Shapeev, Maxim Rakhuba +1
We propose a parameter-reduced version of magnetic Moment Tensor Potential (mMTP) based on various matrix and tensor decompositions. The resulting compressed magnetic machine-learn…
Low-rank approximation of Moment Tensor Potential enables reducing training set size without loss of accuracy
Anna Bondarenko, Nikita Rybin, Maxim Rakhuba +1
In this study, we implement a low-rank approximation of Moment Tensor Potential (MTP) based on the tensor train (TT) decomposition. The implemented tensor-factorized MTP (TFMTP) an…
Tensorion: A Tensor-Aware Generalization of the Muon Optimizer
Vladimir Bogachev, Vladimir Aletov, Alexander Molozhavenko +2
Common first-order optimizers, such as Adam, implicitly treat each parameter block as an unstructured vector, which disregards the multilinear weight structure present in many mode…
Low-rank matrix and tensor approximations for compression of machine-learning interatomic potentials
Igor Vorotnikov, Fedor Romashov, Nikita Rybin +2
Machine-learning interatomic potentials (MLIPs) have become a mainstay in computationally-guided materials science, surpassing traditional force fields due to their flexible functi…
Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation
Mikhail Usvyatsov, Anastasia Makarova, Rafael Ballester-Ripoll +3
We propose an end-to-end trainable framework that processes large-scale visual data tensors by looking at a fraction of their entries only. Our method combines a neural network enc…