Zobrazeno 1 - 10
of 3 402
pro vyhledávání: '"Johannes, S."'
Autor:
Fuest, Michael, Ma, Pingchuan, Gui, Ming, Fischer, Johannes S., Hu, Vincent Tao, Ommer, Bjorn
Diffusion Models are popular generative modeling methods in various vision tasks, attracting significant attention. They can be considered a unique instance of self-supervised learning methods due to their independence from label annotation. This sur
Externí odkaz:
http://arxiv.org/abs/2407.00783
Autor:
Gui, Ming, Fischer, Johannes S., Prestel, Ulrich, Ma, Pingchuan, Kotovenko, Dmytro, Grebenkova, Olga, Baumann, Stefan Andreas, Hu, Vincent Tao, Ommer, Björn
Monocular depth estimation is crucial for numerous downstream vision tasks and applications. Current discriminative approaches to this problem are limited due to blurry artifacts, while state-of-the-art generative methods suffer from slow sampling du
Externí odkaz:
http://arxiv.org/abs/2403.13788
Publikováno v:
Phys. Rev. B 110, L041105 (2024)
Multi-orbital electronic models hosting a non-trivial band-topology in the regime of strong electronic interactions are an ideal playground for exploring a host of complex phenomenology. We consider here a sign-problem-free and time-reversal symmetri
Externí odkaz:
http://arxiv.org/abs/2402.09506
Autor:
Fischer, Johannes S., Gui, Ming, Ma, Pingchuan, Stracke, Nick, Baumann, Stefan A., Ommer, Björn
Recently, there has been tremendous progress in visual synthesis and the underlying generative models. Here, diffusion models (DMs) stand out particularly, but lately, flow matching (FM) has also garnered considerable interest. While DMs excel in pro
Externí odkaz:
http://arxiv.org/abs/2312.07360
Publikováno v:
SciPost Phys. Core 7, 028 (2024)
We present a numerical quantum Monte Carlo (QMC) method for simulating the 3D phase transition on the recently proposed fuzzy sphere [Phys. Rev. X 13, 021009 (2023)]. By introducing an additional $SU(2)$ layer degree of freedom, we reformulate the mo
Externí odkaz:
http://arxiv.org/abs/2310.19880
To analyze the scaling potential of deep tabular representation learning models, we introduce a novel Transformer-based architecture specifically tailored to tabular data and cross-table representation learning by utilizing table-specific tokenizers
Externí odkaz:
http://arxiv.org/abs/2309.17339
Autor:
Siems, Julien, Ditschuneit, Konstantin, Ripken, Winfried, Lindborg, Alma, Schambach, Maximilian, Otterbach, Johannes S., Genzel, Martin
Generalized Additive Models (GAMs) have recently experienced a resurgence in popularity due to their interpretability, which arises from expressing the target value as a sum of non-linear transformations of the features. Despite the current enthusias
Externí odkaz:
http://arxiv.org/abs/2305.11475