Zobrazeno 1 - 10
of 4 677
pro vyhledávání: '"Baranchuk A"'
Autor:
Persiianov, Mikhail, Asadulaev, Arip, Andreev, Nikita, Starodubcev, Nikita, Baranchuk, Dmitry, Kratsios, Anastasis, Burnaev, Evgeny, Korotin, Alexander
Learning conditional distributions $\pi^*(\cdot|x)$ is a central problem in machine learning, which is typically approached via supervised methods with paired data $(x,y) \sim \pi^*$. However, acquiring paired data samples is often challenging, espec
Externí odkaz:
http://arxiv.org/abs/2410.02628
Autor:
Simhadri, Harsha Vardhan, Aumüller, Martin, Ingber, Amir, Douze, Matthijs, Williams, George, Manohar, Magdalen Dobson, Baranchuk, Dmitry, Liberty, Edo, Liu, Frank, Landrum, Ben, Karjikar, Mazin, Dhulipala, Laxman, Chen, Meng, Chen, Yue, Ma, Rui, Zhang, Kai, Cai, Yuzheng, Shi, Jiayang, Chen, Yizhuo, Zheng, Weiguo, Wan, Zihao, Yin, Jie, Huang, Ben
The 2023 Big ANN Challenge, held at NeurIPS 2023, focused on advancing the state-of-the-art in indexing data structures and search algorithms for practical variants of Approximate Nearest Neighbor (ANN) search that reflect the growing complexity and
Externí odkaz:
http://arxiv.org/abs/2409.17424
Autor:
Egiazarian, Vage, Kuznedelev, Denis, Voronov, Anton, Svirschevski, Ruslan, Goin, Michael, Pavlov, Daniil, Alistarh, Dan, Baranchuk, Dmitry
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 production-grade diffusion models that consistently increase in size and al
Externí odkaz:
http://arxiv.org/abs/2409.00492
Diffusion distillation represents a highly promising direction for achieving faithful text-to-image generation in a few sampling steps. However, despite recent successes, existing distilled models still do not provide the full spectrum of diffusion a
Externí odkaz:
http://arxiv.org/abs/2406.14539
Autor:
Motwani, Sumeet Ramesh, Baranchuk, Mikhail, Strohmeier, Martin, Bolina, Vijay, Torr, Philip H. S., Hammond, Lewis, de Witt, Christian Schroeder
Recent capability increases in large language models (LLMs) open up applications in which groups of communicating generative AI agents solve joint tasks. This poses privacy and security challenges concerning the unauthorised sharing of information, o
Externí odkaz:
http://arxiv.org/abs/2402.07510
Knowledge distillation methods have recently shown to be a promising direction to speedup the synthesis of large-scale diffusion models by requiring only a few inference steps. While several powerful distillation methods were recently proposed, the o
Externí odkaz:
http://arxiv.org/abs/2312.10835
Autor:
Borzunov, Alexander, Ryabinin, Max, Chumachenko, Artem, Baranchuk, Dmitry, Dettmers, Tim, Belkada, Younes, Samygin, Pavel, Raffel, Colin
Large language models (LLMs) are useful in many NLP tasks and become more capable with size, with the best open-source models having over 50 billion parameters. However, using these 50B+ models requires high-end hardware, making them inaccessible to
Externí odkaz:
http://arxiv.org/abs/2312.08361
The statistical distribution of content uploaded and searched on media sharing sites changes over time due to seasonal, sociological and technical factors. We investigate the impact of this "content drift" for large-scale similarity search tools, bas
Externí odkaz:
http://arxiv.org/abs/2308.02752
Recent advances in diffusion models enable many powerful instruments for image editing. One of these instruments is text-driven image manipulations: editing semantic attributes of an image according to the provided text description. % Popular text-co
Externí odkaz:
http://arxiv.org/abs/2304.04344
Publikováno v:
Proceedings of the 40 th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023
Denoising diffusion probabilistic models are currently becoming the leading paradigm of generative modeling for many important data modalities. Being the most prevalent in the computer vision community, diffusion models have also recently gained some
Externí odkaz:
http://arxiv.org/abs/2209.15421