1 code implementation • 12 Apr 2024 • Nils Lehmann, Nina Maria Gottschling, Stefan Depeweg, Eric Nalisnick
We provide a detailed evaluation of predictive uncertainty estimates from state-of-the-art uncertainty quantification (UQ) methods for DNNs.
no code implementations • 12 Apr 2018 • Stefan Depeweg, Constantin A. Rothkopf, Frank Jäkel
More than 50 years ago Bongard introduced 100 visual concept learning problems as a testbed for intelligent vision systems.
no code implementations • 10 Dec 2017 • Stefan Depeweg, José Miguel Hernández-Lobato, Steffen Udluft, Thomas Runkler
We derive a novel sensitivity analysis of input variables for predictive epistemic and aleatoric uncertainty.
1 code implementation • ICML 2018 • Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
Bayesian neural networks with latent variables are scalable and flexible probabilistic models: They account for uncertainty in the estimation of the network weights and, by making use of latent variables, can capture complex noise patterns in the data.
2 code implementations • 27 Sep 2017 • Daniel Hein, Stefan Depeweg, Michel Tokic, Steffen Udluft, Alexander Hentschel, Thomas A. Runkler, Volkmar Sterzing
On one hand, these benchmarks are designed to provide interpretable RL training scenarios and detailed insight into the learning process of the method on hand.
no code implementations • 26 Jun 2017 • Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
Bayesian neural networks (BNNs) with latent variables are probabilistic models which can automatically identify complex stochastic patterns in the data.
2 code implementations • 23 May 2016 • Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
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.
Model-based Reinforcement Learning reinforcement-learning +2