Search Results for author: Tim Genewein

Found 16 papers, 3 papers with code

Model-Free Risk-Sensitive Reinforcement Learning

no code implementations4 Nov 2021 Grégoire Delétang, Jordi Grau-Moya, Markus Kunesch, Tim Genewein, Rob Brekelmans, Shane Legg, Pedro A. Ortega

Since the Gaussian free energy is known to be a certainty-equivalent sensitive to the mean and the variance, the learning rule has applications in risk-sensitive decision-making.

Decision Making

Stochastic Approximation of Gaussian Free Energy for Risk-Sensitive Reinforcement Learning

no code implementations NeurIPS 2021 Grégoire Delétang, Jordi Grau-Moya, Markus Kunesch, Tim Genewein, Rob Brekelmans, Shane Legg, Pedro A Ortega

Since the Gaussian free energy is known to be a certainty-equivalent sensitive to the mean and the variance, the learning rule has applications in risk-sensitive decision-making.

Decision Making

Causal Analysis of Agent Behavior for AI Safety

no code implementations5 Mar 2021 Grégoire Déletang, Jordi Grau-Moya, Miljan Martic, Tim Genewein, Tom McGrath, Vladimir Mikulik, Markus Kunesch, Shane Legg, Pedro A. Ortega

As machine learning systems become more powerful they also become increasingly unpredictable and opaque.

Meta-trained agents implement Bayes-optimal agents

no code implementations NeurIPS 2020 Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega

Memory-based meta-learning is a powerful technique to build agents that adapt fast to any task within a target distribution.

Meta-Learning

Group Pruning using a Bounded-Lp norm for Group Gating and Regularization

no code implementations9 Aug 2019 Chaithanya Kumar Mummadi, Tim Genewein, Dan Zhang, Thomas Brox, Volker Fischer

We achieve state-of-the-art pruning results for ResNet-50 with higher accuracy on ImageNet.

Sinkhorn AutoEncoders

2 code implementations ICLR 2019 Giorgio Patrini, Rianne van den Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, Frank Nielsen

We show that minimizing the p-Wasserstein distance between the generator and the true data distribution is equivalent to the unconstrained min-min optimization of the p-Wasserstein distance between the encoder aggregated posterior and the prior in latent space, plus a reconstruction error.

Probabilistic Programming

The Power of Ensembles for Active Learning in Image Classification

no code implementations CVPR 2018 William H. Beluch, Tim Genewein, Andreas Nürnberger, Jan M. Köhler

To investigate why Monte-Carlo Dropout uncertainties perform worse, we explore potential differences in isolation in a series of experiments.

Active Learning General Classification +1

An information-theoretic on-line update principle for perception-action coupling

no code implementations16 Apr 2018 Zhen Peng, Tim Genewein, Felix Leibfried, Daniel A. Braun

Here we consider perception and action as two serial information channels with limited information-processing capacity.

Variational Network Quantization

no code implementations ICLR 2018 Jan Achterhold, Jan Mathias Koehler, Anke Schmeink, Tim Genewein

In this paper, the preparation of a neural network for pruning and few-bit quantization is formulated as a variational inference problem.

Quantization Variational Inference

On Detecting Adversarial Perturbations

1 code implementation14 Feb 2017 Jan Hendrik Metzen, Tim Genewein, Volker Fischer, Bastian Bischoff

In this work, we propose to augment deep neural networks with a small "detector" subnetwork which is trained on the binary classification task of distinguishing genuine data from data containing adversarial perturbations.

Adversarial Attack General Classification

Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes

no code implementations7 Apr 2016 Jordi Grau-Moya, Felix Leibfried, Tim Genewein, Daniel A. Braun

As limit cases, this generalized scheme includes standard value iteration with a known model, Bayesian MDP planning, and robust planning.

Abstraction in decision-makers with limited information processing capabilities

no code implementations16 Dec 2013 Tim Genewein, Daniel A. Braun

A distinctive property of human and animal intelligence is the ability to form abstractions by neglecting irrelevant information which allows to separate structure from noise.

Decision Making

A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function

no code implementations NeurIPS 2012 Pedro Ortega, Jordi Grau-Moya, Tim Genewein, David Balduzzi, Daniel Braun

We propose a novel Bayesian approach to solve stochastic optimization problems that involve finding extrema of noisy, nonlinear functions.

Stochastic Optimization

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