Zobrazeno 1 - 10
of 358
pro vyhledávání: '"Sendera A"'
Function-space priors in Bayesian Neural Networks provide a more intuitive approach to embedding beliefs directly into the model's output, thereby enhancing regularization, uncertainty quantification, and risk-aware decision-making. However, imposing
Externí odkaz:
http://arxiv.org/abs/2410.15777
Low-rank adaptation (LoRA) is a fine-tuning technique that can be applied to conditional generative diffusion models. LoRA utilizes a small number of context examples to adapt the model to a specific domain, character, style, or concept. However, due
Externí odkaz:
http://arxiv.org/abs/2410.03941
Autor:
Venkatraman, Siddarth, Jain, Moksh, Scimeca, Luca, Kim, Minsu, Sendera, Marcin, Hasan, Mohsin, Rowe, Luke, Mittal, Sarthak, Lemos, Pablo, Bengio, Emmanuel, Adam, Alexandre, Rector-Brooks, Jarrid, Bengio, Yoshua, Berseth, Glen, Malkin, Nikolay
Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of
Externí odkaz:
http://arxiv.org/abs/2405.20971
Autor:
Akhound-Sadegh, Tara, Rector-Brooks, Jarrid, Bose, Avishek Joey, Mittal, Sarthak, Lemos, Pablo, Liu, Cheng-Hao, Sendera, Marcin, Ravanbakhsh, Siamak, Gidel, Gauthier, Bengio, Yoshua, Malkin, Nikolay, Tong, Alexander
Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem in science. In this paper, we propose Iterated Denoising Energy Matchi
Externí odkaz:
http://arxiv.org/abs/2402.06121
Autor:
Sendera, Marcin, Kim, Minsu, Mittal, Sarthak, Lemos, Pablo, Scimeca, Luca, Rector-Brooks, Jarrid, Adam, Alexandre, Bengio, Yoshua, Malkin, Nikolay
We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function. We benchmark several diffusion-structured inference methods, including simulation-based variational approaches and o
Externí odkaz:
http://arxiv.org/abs/2402.05098
Autor:
Sendera, Marcin, Przewięźlikowski, Marcin, Karanowski, Konrad, Zięba, Maciej, Tabor, Jacek, Spurek, Przemysław
Few-shot models aim at making predictions using a minimal number of labeled examples from a given task. The main challenge in this area is the one-shot setting where only one element represents each class. We propose HyperShot - the fusion of kernels
Externí odkaz:
http://arxiv.org/abs/2203.11378
Autor:
Sendera, Marcin, Tabor, Jacek, Nowak, Aleksandra, Bedychaj, Andrzej, Patacchiola, Massimiliano, Trzciński, Tomasz, Spurek, Przemysław, Zięba, Maciej
Gaussian Processes (GPs) have been widely used in machine learning to model distributions over functions, with applications including multi-modal regression, time-series prediction, and few-shot learning. GPs are particularly useful in the last appli
Externí odkaz:
http://arxiv.org/abs/2110.13561
Autor:
Sendera, Marcin, Śmieja, Marek, Maziarka, Łukasz, Struski, Łukasz, Spurek, Przemysław, Tabor, Jacek
We propose FlowSVDD -- a flow-based one-class classifier for anomaly/outliers detection that realizes a well-known SVDD principle using deep learning tools. Contrary to other approaches to deep SVDD, the proposed model is instantiated using flow-base
Externí odkaz:
http://arxiv.org/abs/2108.04907
Autor:
Sendera, Anna, Adamczyk-Grochala, Jagoda, Pikuła, Barbara, Cholewa, Marian, Banaś-Ząbczyk, Agnieszka
Publikováno v:
In Toxicology in Vitro March 2024 95
Autor:
Trojniak, Julia1 (AUTHOR) juliatrojniak0@gmail.com, Sendera, Anna2 (AUTHOR) antrzyna@ur.edu.pl, Banaś-Ząbczyk, Agnieszka2 (AUTHOR) agnieszkabanas@o2.pl, Kopańska, Marta3 (AUTHOR) martakopanska@poczta.onet.pl
Publikováno v:
International Journal of Molecular Sciences. Jun2024, Vol. 25 Issue 11, p6240. 24p.