26 citations · 65 across the 23 of their papers we have counts for
9 papers · 1 filter
Label Privacy in Split Learning for Large Models with Parameter-Efficient Training
Philip Zmushko, Marat Mansurov, Ruslan Svirschevski +3
As deep learning models become larger and more expensive, many practitioners turn to fine-tuning APIs. These web services allow fine-tuning a model between two parties: the client…
Switti: Designing Scale-Wise Transformers for Text-to-Image Synthesis
Anton Voronov, Denis Kuznedelev, Mikhail Khoroshikh +2
This work presents Switti, a scale-wise transformer for text-to-image generation. We start by adapting an existing next-scale prediction autoregressive (AR) architecture to T2I gen…
EvoPress: Accurate Dynamic Model Compression via Evolutionary Search
Oliver Sieberling, Denis Kuznedelev, Eldar Kurtic +1
The high computational costs of large language models (LLMs) have led to a flurry of research on LLM compression, via methods such as quantization, sparsification, or structured pr…
Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization
Vage Egiazarian, Denis Kuznedelev, Anton Voronov +5
Text-to-image diffusion models have emerged as a powerful framework for high-quality image generation given textual prompts. Their success has driven the rapid development of produ…
The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information
Diyuan Wu, Ionut-Vlad Modoranu, Mher Safaryan +2
The rising footprint of machine learning has led to a focus on imposing \emph{model sparsity} as a means of reducing computational and memory costs. For deep neural networks (DNNs)…
PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression
Vladimir Malinovskii, Denis Mazur, Ivan Ilin +5
There has been significant interest in "extreme" compression of large language models (LLMs), i.e., to 1-2 bits per parameter, which allows such models to be executed efficiently o…
Does Diffusion Beat GAN in Image Super Resolution?
Denis Kuznedelev, Valerii Startsev, Daniil Shlenskii +1
There is a prevalent opinion that diffusion-based models outperform GAN-based counterparts in the Image Super Resolution (ISR) problem. However, in most studies, diffusion-based IS…
YaART: Yet Another ART Rendering Technology
Sergey Kastryulin, Artem Konev, Alexander Shishenya +20
In the rapidly progressing field of generative models, the development of efficient and high-fidelity text-to-image diffusion systems represents a significant frontier. This study…
Extreme Compression of Large Language Models via Additive Quantization
Vage Egiazarian, Andrei Panferov, Denis Kuznedelev +3
The emergence of accurate open large language models (LLMs) has led to a race towards performant quantization techniques which can enable their execution on end-user devices. In th…