14 citations · 16 across the 19 of their papers we have counts for
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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…
On the Upper Bounds for the Matrix Spectral Norm
Alexey Naumov, Maxim Rakhuba, Denis Ryapolov +1
We consider the problem of estimating the spectral norm of a matrix using only matrix-vector products. We propose a new Counterbalance estimator that provides upper bounds on the n…
Accelerating Newton-Schulz Iteration for Orthogonalization via Chebyshev-type Polynomials
Ekaterina Grishina, Matvey Smirnov, Maxim Rakhuba
The problem of computing optimal orthogonal approximation to a given matrix has attracted growing interest in machine learning. Notable applications include the recent Muon optimiz…