Search Results for author: Stefan Depeweg

Found 6 papers, 3 papers with code

Solving Bongard Problems with a Visual Language and Pragmatic Reasoning

no code implementations12 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.

Bayesian Inference

Sensitivity Analysis for Predictive Uncertainty in Bayesian Neural Networks

no code implementations10 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.

Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning

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.

Active Learning Decision Making +2

A Benchmark Environment Motivated by Industrial Control Problems

2 code implementations27 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.

OpenAI Gym Reinforcement Learning (RL)

Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables

no code implementations26 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.

Active Learning reinforcement-learning +2

Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks

2 code implementations23 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

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