Extreme Compression of Large Language Models via Additive Quantization

1 code implementation • 11 Jan 2024 • Vage Egiazarian, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, Dan Alistarh

The emergence of accurate open large language models (LLMs) has led to a race towards quantization techniques for such models enabling execution on end-user devices.

Quantization

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

1 code implementation • 5 Jun 2023 • Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh

Recent advances in large language model (LLM) pretraining have led to high-quality LLMs with impressive abilities.

Language Modelling Large Language Model +1

Neural Optimal Transport with General Cost Functionals

no code implementations • 30 May 2022 • Arip Asadulaev, Alexander Korotin, Vage Egiazarian, Petr Mokrov, Evgeny Burnaev

We introduce a novel neural network-based algorithm to compute optimal transport (OT) plans for general cost functionals.

Wasserstein Iterative Networks for Barycenter Estimation

1 code implementation • 28 Jan 2022 • Alexander Korotin, Vage Egiazarian, Lingxiao Li, Evgeny Burnaev

Wasserstein barycenters have become popular due to their ability to represent the average of probability measures in a geometrically meaningful way.

DEF: Deep Estimation of Sharp Geometric Features in 3D Shapes

1 code implementation • 30 Nov 2020 • Albert Matveev, Ruslan Rakhimov, Alexey Artemov, Gleb Bobrovskikh, Vage Egiazarian, Emil Bogomolov, Daniele Panozzo, Denis Zorin, Evgeny Burnaev

We propose Deep Estimators of Features (DEFs), a learning-based framework for predicting sharp geometric features in sampled 3D shapes.

Deep Vectorization of Technical Drawings

1 code implementation • ECCV 2020 • Vage Egiazarian, Oleg Voynov, Alexey Artemov, Denis Volkhonskiy, Aleksandr Safin, Maria Taktasheva, Denis Zorin, Evgeny Burnaev

We present a new method for vectorization of technical line drawings, such as floor plans, architectural drawings, and 2D CAD images.

Latent-Space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds

1 code implementation • 13 Dec 2019 • Vage Egiazarian, Savva Ignatyev, Alexey Artemov, Oleg Voynov, Andrey Kravchenko, Youyi Zheng, Luiz Velho, Evgeny Burnaev

Constructing high-quality generative models for 3D shapes is a fundamental task in computer vision with diverse applications in geometry processing, engineering, and design.

Generating 3D Point Clouds Representation Learning

Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs

1 code implementation • NeurIPS 2019 • Denis Mazur, Vage Egiazarian, Stanislav Morozov, Artem Babenko

Our main contribution is PRODIGE: a method that learns a weighted graph representation of data end-to-end by gradient descent.

Collaborative Filtering Inductive Bias +1

Wasserstein-2 Generative Networks

4 code implementations • ICLR 2021 • Alexander Korotin, Vage Egiazarian, Arip Asadulaev, Alexander Safin, Evgeny Burnaev

We propose a novel end-to-end non-minimax algorithm for training optimal transport mappings for the quadratic cost (Wasserstein-2 distance).

Domain Adaptation Style Transfer

Perceptual deep depth super-resolution

1 code implementation • ICCV 2019 • Oleg Voynov, Alexey Artemov, Vage Egiazarian, Alexander Notchenko, Gleb Bobrovskikh, Denis Zorin, Evgeny Burnaev

RGBD images, combining high-resolution color and lower-resolution depth from various types of depth sensors, are increasingly common.

Super-Resolution