Zobrazeno 1 - 10
of 10 795
pro vyhledávání: '"A. Babenko"'
Deep learning architectures for supervised learning on tabular data range from simple multilayer perceptrons (MLP) to sophisticated Transformers and retrieval-augmented methods. This study highlights a major, yet so far overlooked opportunity for sub
Externí odkaz:
http://arxiv.org/abs/2410.24210
For non-empty sets X we define notions of distance and pseudo metric with values in a partially ordered set that has a smallest element $\theta $. If $h_X$ is a distance in $X$ (respectively, a pseudo metric in $X$), then the pair $(X,h_X)$ is called
Externí odkaz:
http://arxiv.org/abs/2408.15579
Autor:
Ahmed, Faruk, Sellergren, Andrew, Yang, Lin, Xu, Shawn, Babenko, Boris, Ward, Abbi, Olson, Niels, Mohtashamian, Arash, Matias, Yossi, Corrado, Greg S., Duong, Quang, Webster, Dale R., Shetty, Shravya, Golden, Daniel, Liu, Yun, Steiner, David F., Wulczyn, Ellery
Microscopic interpretation of histopathology images underlies many important diagnostic and treatment decisions. While advances in vision-language modeling raise new opportunities for analysis of such images, the gigapixel-scale size of whole slide i
Externí odkaz:
http://arxiv.org/abs/2406.19578
Advances in machine learning research drive progress in real-world applications. To ensure this progress, it is important to understand the potential pitfalls on the way from a novel method's success on academic benchmarks to its practical deployment
Externí odkaz:
http://arxiv.org/abs/2406.19380
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:
Bennett, Chloe R., Cole-Lewis, Heather, Farquhar, Stephanie, Haamel, Naama, Babenko, Boris, Lang, Oran, Fleck, Mat, Traynis, Ilana, Lau, Charles, Horn, Ivor, Lyles, Courtney
The field of artificial intelligence (AI) is rapidly influencing health and healthcare, but bias and poor performance persists for populations who face widespread structural oppression. Previous work has clearly outlined the need for more rigorous at
Externí odkaz:
http://arxiv.org/abs/2406.18563
Autor:
Kastryulin, Sergey, Konev, Artem, Shishenya, Alexander, Lyapustin, Eugene, Khurshudov, Artem, Tselousov, Alexander, Vinokurov, Nikita, Kuznedelev, Denis, Markovich, Alexander, Livshits, Grigoriy, Kirillov, Alexey, Tabisheva, Anastasiia, Chubarova, Liubov, Kaminskaia, Marina, Ustyuzhanin, Alexander, Shvetsov, Artemii, Shlenskii, Daniil, Startsev, Valerii, Kornilov, Dmitrii, Romanov, Mikhail, Babenko, Artem, Ovcharenko, Sergei, Khrulkov, Valentin
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 introduces YaART, a novel production-grade text-to-image cascaded diff
Externí odkaz:
http://arxiv.org/abs/2404.05666
This paper introduces a new data-driven, non-parametric method for image quality and aesthetics assessment, surpassing existing approaches and requiring no prompt engineering or fine-tuning. We eliminate the need for expressive textual embeddings by
Externí odkaz:
http://arxiv.org/abs/2403.06866
Autor:
Egiazarian, Vage, Panferov, Andrei, Kuznedelev, Denis, Frantar, Elias, Babenko, Artem, Alistarh, Dan
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 this paper, we revisit the problem of "extreme" LLM compression-defined
Externí odkaz:
http://arxiv.org/abs/2401.06118
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