8 citations · 13 across the 9 of their papers we have counts for
10 papers
PERELMAN: Pipeline for scientific literature meta-analysis. Technical report
Daniil Sherki, Daniil Merkulov, Alexandra Savina +1
We present PERELMAN (PipEline foR sciEntific Literature Meta-ANalysis), an agentic framework designed to extract specific information from a large corpus of scientific articles to…
Ky Fan Norms and Beyond: Dual Norms and Combinations for Matrix Optimization
Alexey Kravatskiy, Ivan Kozyrev, Nikolai Kozlov +3
In this article, we explore the use of various matrix norms for optimizing functions of weight matrices, a crucial problem in deep learning. Moving beyond the spectral norm that un…
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…
Another approach to build Lyapunov functions for the first order methods in the quadratic case
Daniil Merkulov, Ivan Oseledets
Lyapunov functions play a fundamental role in analyzing the stability and convergence properties of optimization methods. In this paper, we propose a novel and straightforward appr…
NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizer
Valentin Leplat, Daniil Merkulov, Aleksandr Katrutsa +3
Classical machine learning models such as deep neural networks are usually trained by using Stochastic Gradient Descent-based (SGD) algorithms. The classical SGD can be interpreted…
Memory-Efficient Backpropagation through Large Linear Layers
Daniel Bershatsky, Aleksandr Mikhalev, Alexandr Katrutsa +3
In modern neural networks like Transformers, linear layers require significant memory to store activations during backward pass. This study proposes a memory reduction approach to…