Perceptually-based single-image depth super-resolution. - O. Voinov; Alexey Artemov; Vage Egiazarian; Alexandr Notchenko; Gleb Bobrovskikh; Denis Zorin; Evgeniy Burnaev (CoRR, 2018)

Perceptual Deep Depth Super-Resolution. - Oleg Voynov; Alexey Artemov; Vage Egiazarian; Alexandr Notchenko; Gleb Bobrovskikh; Evgeny Burnaev; Denis Zorin (ICCV, 2019)

Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs. - Denis Mazur; Vage Egiazarian; Stanislav Morozov; Artem Babenko (NeurIPS, 2019)

Wasserstein-2 Generative Networks. - Alexander Korotin; Vage Egiazarian; Arip Asadulaev; Evgeny Burnaev (CoRR, 2019)

Beyond Vector Spaces: Compact Data Representationas Differentiable Weighted Graphs. - Denis Mazur; Vage Egiazarian; Stanislav Morozov; Artem Babenko (CoRR, 2019)

Deep Vectorization of Technical Drawings. - Vage Egiazarian; Oleg Voynov; Alexey Artemov; Denis Volkhonskiy; Aleksandr Safin; Maria Taktasheva; Denis Zorin; Evgeny Burnaev (ECCV (13), 2020)

Latent-space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds. - Vage Egiazarian; Savva Ignatyev; Alexey Artemov; Oleg Voynov; Andrey Kravchenko; Youyi Zheng; Luiz Velho 001; Evgeny Burnaev (VISIGRAPP (4: VISAPP), 2020)

Wasserstein-2 Generative Networks. - Alexander Korotin; Vage Egiazarian; Arip Asadulaev; Alexander Safin; Evgeny Burnaev (ICLR, 2021)

Wasserstein Iterative Networks for Barycenter Estimation. - Alexander Korotin; Vage Egiazarian; Lingxiao Li; Evgeny Burnaev (NeurIPS, 2022)

Neural Optimal Transport with General Cost Functionals. - Arip Asadulaev; Alexander Korotin; Vage Egiazarian; Evgeny Burnaev (CoRR, 2022)

DEF: deep estimation of sharp geometric features in 3D shapes. - Albert Matveev; Ruslan Rakhimov; Alexey Artemov; Gleb Bobrovskikh; Vage Egiazarian; Emil Bogomolov; Daniele Panozzo; Denis Zorin; Evgeny Burnaev (ACM Trans. Graph., 2022)

Neural Optimal Transport with General Cost Functionals. - Arip Asadulaev; Alexander Korotin; Vage Egiazarian; Petr Mokrov; Evgeny Burnaev (ICLR, 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 (ICLR, 2024)

Extreme Compression of Large Language Models via Additive Quantization. - Vage Egiazarian; Andrei Panferov; Denis Kuznedelev; Elias Frantar; Artem Babenko; Dan Alistarh (ICML, 2024)

Rethinking Optimal Transport in Offline Reinforcement Learning. - Arip Asadulaev; Rostislav Korst; Aleksandr Korotin; Vage Egiazarian; Andrey Filchenkov; Evgeny Burnaev (NeurIPS, 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)

Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models. - Alina Shutova; Vladimir Malinovskii; Vage Egiazarian; Denis Kuznedelev; Denis Mazur; Nikita Surkov; Ivan Ermakov; Dan Alistarh (CoRR, 2025)

Hogwild! Inference: Parallel LLM Generation via Concurrent Attention. - Gleb Rodionov; Roman Garipov; Alina Shutova; George Yakushev; Vage Egiazarian; Anton Sinitsin; Denis Kuznedelev; Dan Alistarh (CoRR, 2025)

AutoJudge: Judge Decoding Without Manual Annotation. - Roman Garipov; Fedor Velikonivtsev; Ruslan Svirschevski; Vage Egiazarian; Max Ryabinin (CoRR, 2025)

Unified Scaling Laws for Compressed Representations. - Andrei Panferov; Alexandra Volkova; Ionut-Vlad Modoranu; Vage Egiazarian; Mher Safaryan; Dan Alistarh (CoRR, 2025)