1 citations · 1 across the 10 of their papers we have counts for
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Just a Simple Transformation is Enough for Data Protection in Vertical Federated Learning
Andrei Semenov, Philip Zmushko, Alexander Pichugin +1
Vertical Federated Learning (VFL) aims to enable collaborative training of deep learning models while maintaining privacy protection. However, the VFL procedure still has component…
Mixed Newton Method for Optimization in Complex Spaces
Nikita Yudin, Roland Hildebrand, Sergey Bakhurin +5
In this paper, we modify and apply the recently introduced Mixed Newton Method, which is originally designed for minimizing real-valued functions of complex variables, to the minim…
Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed
Savelii Chezhegov, Yaroslav Klyukin, Andrei Semenov +5
Methods with adaptive stepsizes, such as AdaGrad and Adam, are essential for training modern Deep Learning models, especially Large Language Models. Typically, the noise in the sto…
Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning
Andrei Semenov, Vladimir Ivanov, Aleksandr Beznosikov +1
We propose a novel architecture and method of explainable classification with Concept Bottleneck Models (CBMs). While SOTA approaches to Image Classification task work as a black b…