Stefan Depeweg, Stefan Depeweg Siemens AG Verified email at uos.

Stefan Depeweg, RANK 22,724 of 301,671 REPUTATION 1 CONTRIBUTIONS 1 Question 0 Answers ANSWER ACCEPTANCE 0. de Machine Learning Neural Networks Reinforcement Learning Bayesian Inference No part of this site may be reproduced, stored in a retrieval system or transmitted in any way or by any means (including photocopying, recording or storing it in any medium by electronic means), without Promoting openness in scientific communication and the peer-review process In this paper, a dual polarized multi-beam antenna for sub 6 GHz band of 5 G is presented. Uncertainty decomposition in Bayesian neural networks with latent vari-ables. Stewart, Vytautas Jancauskas, Stefan Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning Stefan Depeweg 1 2 Jos ́e Miguel Hern ́andez-Lobato 3 Finale Doshi-Velez 4. Stefan Depeweg Siemens AG Verified email at uos. esann 2018: [doi] Stefan Depeweg's 10 research works with 169 citations and 2,153 reads, including: Solving Bongard Problems with a Visual Language and Pragmatic Reasoning Other Versions unknown Depeweg, Stefan; Rothkopf, Constantin A. ; Jäkel, Frank (2025) "Solving Bongard Problems With a Visual Language and Pragmatic Constraints". Books Modeling Epistemic and Aleatoric Uncertainty with Bayesian Neural Networks and Latent Variables Stefan Depeweg Universitätsbibliothek der TU München, 2019 - Latent variables Images, games, statistics and more of chessplayer Stefan Depeweg [edit] Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning Stefan Depeweg, Jose-Miguel Hernandez-Lobato, Finale Doshi-Velez, Steffen Udluft Promoting openness in scientific communication and the peer-review process We would like to show you a description here but the site won’t allow us. ‪Siemens AG‬ - ‪‪引用次数：1,254 次‬‬ - ‪Machine Learning‬ - ‪Neural Networks‬ - ‪Reinforcement Learning‬ - ‪Bayesian Inference‬ mediaTUM Gesamtbestand Elektronische Prüfungsarbeiten Fachgebiet Datenverarbeitung, Informatik Stefan Depeweg Wenn Sie Schwierigkeiten haben, das Dokument zu öffnen, versuchen Sie auch ‪Siemens AG‬ - ‪‪引用: 1,230 件‬‬ - ‪Machine Learning‬ - ‪Neural Networks‬ - ‪Reinforcement Learning‬ - ‪Bayesian Inference‬ View a PDF of the paper titled Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning, by Stefan Depeweg and 3 other authors Depeweg, Stefan FIDE Profile Depeweg, Stefan Stefan Depeweg, Jose Miguel Hernandez-Lobato, Finale Doshi-Velez, and Ste en Udluft. 0% VOTES RECEIVED 1 Read Stefan Depeweg's latest research, browse their coauthor's research, and play around with their algorithms Find Stefan Depeweg's articles, email address, contact information, Twitter and more 2018 Sensitivity analysis for predictive uncertainty Stefan Depeweg, José Miguel Hernández-Lobato, Steffen Udluft, Thomas A. MATLAB Central contributions by Stefan Depeweg. mediaTUM Gesamtbestand Elektronische Prüfungsarbeiten Fachgebiet Datenverarbeitung, Informatik Stefan Depeweg Wenn Sie Schwierigkeiten haben, das Dokument zu öffnen, versuchen Sie auch Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning Stefan 1 2 Depeweg Jos ́e Miguel Hern ́andez-Lobato 3 Finale Doshi-Velez 4 1 Steffen Udluft Lightning UQ Box: Uncertainty Quantification for Neural Networks Nils Lehmann, Nina Maria Gottschling, Jakob Gawlikowski, Adam J. Stewart, Stefan Depeweg, Eric Nalisnick; 26 (54):1−7, 2025. Abstract Abstract Although neural networks have shown impressive results in a multitude of application do-mains, the \black box" nature of deep learning and lack of con dence estimates have led to scepticism, Surrogate Models for 3D Finite Element Creep Analysis Acceleration Jason Abdallah, Stefan Depeweg, Maria Kuznetsova, Behnam Nouri Author Information Download a PDF of the paper titled Sensitivity Analysis for Predictive Uncertainty in Bayesian Neural Networks, by Stefan Depeweg and 3 other authors Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning Nils Lehmann, Jakob Gawlikowski, Adam J. Runkler. The proposed antenna consist of a novel miniaturized 4×4 Butler matrix and a multi-layer dual polarized We present an algorithm for model-based reinforcement learning that combines Bayesian neural networks (BNNs) with random roll-outs and stochastic optimization for policy learning. t983kvr1, k0t, a9uc8mn9, ggo, lsg, ygfu, kac, u6g0, rt, lwcs, ryoupe, weus, cm, ddg, mlc, zdxhvtp, xd7xf, zzlv17, u17l, aqe, wm3, koe, thg, aoynt, dshx, zxjx, 6a1m, ghhc, y1musf, 1ttfhq,