2 citations · 4 across the 3 of their papers we have counts for
3 papers
Quantization of Large Language Models with an Overdetermined Basis
Daniil Merkulov, Daria Cherniuk, Alexander Rudikov +4
In this paper, we introduce an algorithm for data quantization based on the principles of Kashin representation. This approach hinges on decomposing any given vector, matrix, or te…
Neural operators meet conjugate gradients: The FCG-NO method for efficient PDE solving
Alexander Rudikov, Vladimir Fanaskov, Ekaterina Muravleva +2
Deep learning solvers for partial differential equations typically have limited accuracy. We propose to overcome this problem by using them as preconditioners. More specifically, w…
Neural functional a posteriori error estimates
Vladimir Fanaskov, Alexander Rudikov, Ivan Oseledets
We propose a new loss function for supervised and physics-informed training of neural networks and operators that incorporates a posteriori error estimate. More specifically, durin…