5 papers
Fast Differentiable SVD on GPU via Polar Decomposition
Uliana Parkina, Askar Tsyganov, Sergei Kudriashov +2
We present a fully GPU-oriented SVD pipeline based on polar decomposition, motivated by iterative methods that rely solely on matrix multiplications, such as the Newton-Schulz iter…
A lower bound for matrix multiplication
Askar Tsyganov, Uliana Parkina, Sergey Samsonov +1
We prove that, over any field, the bilinear complexity of multiplying a matrix by a matrix is strictly greater than . In particular, every exact bili…
Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies
Ekaterina Grishina, Stepan Kuznetsov, Askar Tsyganov +8
The ranking of recommendation algorithms is a challenging problem since model performance is sensitive to dataset characteristics such as sparsity, sequential structure, and scale.…
gfnx: Fast and Scalable Library for Generative Flow Networks in JAX
Daniil Tiapkin, Artem Agarkov, Nikita Morozov +4
In this paper, we present gfnx, a fast and scalable package for training and evaluating Generative Flow Networks (GFlowNets) written in JAX. gfnx provides an extensive set of envir…
Matrix-Free Two-to-Infinity and One-to-Two Norms Estimation
Askar Tsyganov, Evgeny Frolov, Sergey Samsonov +1
In this paper, we propose new randomized algorithms for estimating the two-to-infinity and one-to-two norms in a matrix-free setting, using only matrix-vector multiplications. Our…