Denis Kuznedelev

According to our database, Denis Kuznedelev authored at least 20 papers between 2022 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Accurate Neural Network Pruning Requires Rethinking Sparse Optimization.

Denis Kuznedelev, Eldar Kurtic, Eugenia Iofinova, Elias Frantar, Alexandra Peste, Dan Alistarh

Trans. Mach. Learn. Res., 2024

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training.

Philip Zmushko, Marat Mansurov, Ruslan Svirschevski, Denis Kuznedelev, Max Ryabinin, Aleksandr Beznosikov

CoRR, 2024

Switti: Designing Scale-Wise Transformers for Text-to-Image Synthesis.

Anton Voronov, Denis Kuznedelev, Mikhail Khoroshikh, Valentin Khrulkov, Dmitry Baranchuk

CoRR, 2024

EvoPress: Towards Optimal Dynamic Model Compression via Evolutionary Search.

Oliver Sieberling, Denis Kuznedelev, Eldar Kurtic, Dan Alistarh

CoRR, 2024

Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization.

Vage Egiazarian, Denis Kuznedelev, Anton Voronov, Ruslan Svirschevski, Michael Goin, Daniil Pavlov, Dan Alistarh, Dmitry Baranchuk

CoRR, 2024

The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information.

Diyuan Wu, Ionut-Vlad Modoranu, Mher Safaryan, Denis Kuznedelev, Dan Alistarh

CoRR, 2024

Does Diffusion Beat GAN in Image Super Resolution?

Denis Kuznedelev, Valerii Startsev, Daniil Shlenskii, Sergey Kastryulin

CoRR, 2024

PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression.

Vladimir Malinovskii, Denis Mazur, Ivan Ilin, Denis Kuznedelev, Konstantin Burlachenko, Kai Yi, Dan Alistarh, Peter Richtárik

CoRR, 2024

YaART: Yet Another ART Rendering Technology.

Sergey Kastryulin, Artem Konev, Alexander Shishenya, Eugene Lyapustin, Artem Khurshudov, Alexander Tselousov, Nikita Vinokurov, Denis Kuznedelev, Alexander Markovich, Grigoriy Livshits, Alexey Kirillov, Anastasiia Tabisheva, Liubov Chubarova, Marina Kaminskaia, Alexander Ustyuzhanin, Artemii Shvetsov, Daniil Shlenskii, Valerii Startsev, Dmitrii Kornilov, Mikhail Romanov, Artem Babenko, Sergei Ovcharenko, Valentin Khrulkov

CoRR, 2024

Extreme Compression of Large Language Models via Additive Quantization.

Vage Egiazarian, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, Dan Alistarh

Proceedings of the Forty-first International Conference on Machine Learning, 2024

SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression.

Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Sparse Fine-tuning for Inference Acceleration of Large Language Models.

Eldar Kurtic, Denis Kuznedelev, Elias Frantar, Michael Goin, Dan Alistarh

CoRR, 2023

Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression.

Denis Kuznedelev, Soroush Tabesh, Kimia Noorbakhsh, Elias Frantar, Sara Beery, Eldar Kurtic, Dan Alistarh

CoRR, 2023

Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond.

Oleg Platonov, Denis Kuznedelev, Artem Babenko, Liudmila Prokhorenkova

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models.

Denis Kuznedelev, Eldar Kurtic, Elias Frantar, Dan Alistarh

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts.

Gleb Bazhenov, Denis Kuznedelev, Andrey Malinin, Artem Babenko, Liudmila Prokhorenkova

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A view of mini-batch SGD via generating functions: conditions of convergence, phase transitions, benefit from negative momenta.

Maksim Velikanov, Denis Kuznedelev, Dmitry Yarotsky

Proceedings of the Eleventh International Conference on Learning Representations, 2023

A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Oleg Platonov, Denis Kuznedelev, Michael Diskin, Artem Babenko, Liudmila Prokhorenkova

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

oViT: An Accurate Second-Order Pruning Framework for Vision Transformers.

Denis Kuznedelev, Eldar Kurtic, Elias Frantar, Dan Alistarh

CoRR, 2022

Characterizing Graph Datasets for Node Classification: Beyond Homophily-Heterophily Dichotomy.

Oleg Platonov, Denis Kuznedelev, Artem Babenko, Liudmila Prokhorenkova

CoRR, 2022