Zobrazeno 1 - 10
of 2 464
pro vyhledávání: '"P. Toelle"'
Autor:
Barrera, Gerardo, Tölle, Jonas M.
We establish general conditions for stochastic evolution equations with locally monotone drift and degenerate additive L\'evy noise in variational formulation resulting in the existence of a unique invariant measure for the associated weakly ergodic
Externí odkaz:
http://arxiv.org/abs/2412.01381
Autor:
Tölle, Malte, Burger, Lukas, Kelm, Halvar, André, Florian, Bannas, Peter, Diller, Gerhard, Frey, Norbert, Garthe, Philipp, Groß, Stefan, Hennemuth, Anja, Kaderali, Lars, Krüger, Nina, Leha, Andreas, Martin, Simon, Meyer, Alexander, Nagel, Eike, Orwat, Stefan, Scherer, Clemens, Seiffert, Moritz, Seliger, Jan Moritz, Simm, Stefan, Friede, Tim, Seidler, Tim, Engelhardt, Sandy
Purpose: Federated training is often hindered by heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This is particularly evident in the emerging m
Externí odkaz:
http://arxiv.org/abs/2407.09064
Autor:
Tölle, Malte, Garthe, Philipp, Scherer, Clemens, Seliger, Jan Moritz, Leha, Andreas, Krüger, Nina, Simm, Stefan, Martin, Simon, Eble, Sebastian, Kelm, Halvar, Bednorz, Moritz, André, Florian, Bannas, Peter, Diller, Gerhard, Frey, Norbert, Groß, Stefan, Hennemuth, Anja, Kaderali, Lars, Meyer, Alexander, Nagel, Eike, Orwat, Stefan, Seiffert, Moritz, Friede, Tim, Seidler, Tim, Engelhardt, Sandy
Federated learning (FL) is a renowned technique for utilizing decentralized data while preserving privacy. However, real-world applications often involve inherent challenges such as partially labeled datasets, where not all clients possess expert ann
Externí odkaz:
http://arxiv.org/abs/2407.07557
Autor:
Tölle, Malte, Navarro, Fernando, Eble, Sebastian, Wolf, Ivo, Menze, Bjoern, Engelhardt, Sandy
Federated learning is one popular paradigm to train a joint model in a distributed, privacy-preserving environment. But partial annotations pose an obstacle meaning that categories of labels are heterogeneous over clients. We propose to learn a joint
Externí odkaz:
http://arxiv.org/abs/2407.07488
Within the context of rough path analysis via fractional calculus, we show how the notion of variability can be used to prove the existence of integrals with respect to H\"older continuous multiplicative functionals in the case of Lipschitz coefficie
Externí odkaz:
http://arxiv.org/abs/2407.06907
Autor:
Avelin, Benny, Kuusi, Tuomo, Nummi, Patrik, Saksman, Eero, Tölle, Jonas M., Viitasaari, Lauri
We study periodic solutions to the following divergence-form stochastic partial differential equation with Wick-renormalized gradient on the $d$-dimensional flat torus $\mathbb{T}^d$, \[ -\nabla\cdot\left(e^{\diamond (- \beta X) }\diamond\nabla U\rig
Externí odkaz:
http://arxiv.org/abs/2405.17195
Autor:
Avelin, Benny, Kuusi, Tuomo, Nummi, Patrik, Saksman, Eero, Tölle, Jonas M., Viitasaari, Lauri
We study unique solvability for one dimensional stochastic pressure equation with diffusion coefficient given by the Wick exponential of log-correlated Gaussian fields. We prove well-posedness for Dirichlet, Neumann and periodic boundary data, and th
Externí odkaz:
http://arxiv.org/abs/2402.09127
Autor:
Heidi Toelle, Kęstutis Nastopka
Publikováno v:
Semiotika (Vilniaus universitetas), Vol 12 (2016)
Cet article reprend en partie celui que nous avons publié dans Metai 2014. Nous y procédons tout d’abord, après avoir indiqué nos sources, à une analyse sémantique des vingt-deux lexèmes qui, en arabe classique, désignent divers degrés d
Externí odkaz:
https://doaj.org/article/cee807894c91498384325832f4ac0fbb
Triplet Excitation-Energy Transfer Couplings from Subsystem Time-Dependent Density-Functional Theory
We present an implementation of Triplet Excitation-Energy Transfer (TEET) couplings based on subsystem-based Time-Dependent Density-Functional Theory (sTDDFT). TEET couplings are systematically investigated by comparing "exact" and approximate varian
Externí odkaz:
http://arxiv.org/abs/2312.07123
Robustness against adversarial attacks and distribution shifts is a long-standing goal of Reinforcement Learning (RL). To this end, Robust Adversarial Reinforcement Learning (RARL) trains a protagonist against destabilizing forces exercised by an adv
Externí odkaz:
http://arxiv.org/abs/2311.01642