Tim Genewein | DeepAI

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∙ 05/26/2023

Randomized Positional Encodings Boost Length Generalization of Transformers

Transformers have impressive generalization capabilities on tasks with a...

0 Anian Ruoss, et al. ∙

∙ 02/06/2023

Memory-Based Meta-Learning on Non-Stationary Distributions

Memory-based meta-learning is a technique for approximating Bayes-optima...

0 Tim Genewein, et al. ∙

∙ 09/30/2022

Beyond Bayes-optimality: meta-learning what you know you don't know

Meta-training agents with memory has been shown to culminate in Bayes-op...

8 Jordi Grau-Moya, et al. ∙

∙ 07/05/2022

Neural Networks and the Chomsky Hierarchy

Reliable generalization lies at the heart of safe ML and AI. However, un...

3 Grégoire Delétang, et al. ∙

∙ 03/23/2022

Your Policy Regularizer is Secretly an Adversary

Policy regularization methods such as maximum entropy regularization are...

0 Rob Brekelmans, et al. ∙

∙ 11/04/2021

Model-Free Risk-Sensitive Reinforcement Learning

We extend temporal-difference (TD) learning in order to obtain risk-sens...

9 Grégoire Delétang, et al. ∙

∙ 10/20/2021

Shaking the foundations: delusions in sequence models for interaction and control

The recent phenomenal success of language models has reinvigorated machi...

68 Pedro A. Ortega, et al. ∙

∙ 03/05/2021

Causal Analysis of Agent Behavior for AI Safety

As machine learning systems become more powerful they also become increa...

26 Grégoire Delétang, et al. ∙

∙ 10/23/2020

Algorithms for Causal Reasoning in Probability Trees

Probability trees are one of the simplest models of causal generative pr...

2 Tim Genewein, et al. ∙

∙ 10/21/2020

Meta-trained agents implement Bayes-optimal agents

Memory-based meta-learning is a powerful technique to build agents that ...

8 Vladimir Mikulik, et al. ∙

∙ 08/09/2019

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

Deep neural networks achieve state-of-the-art results on several tasks w...

0 Chaithanya Kumar Mummadi, et al. ∙

∙ 05/08/2019

Meta-learning of Sequential Strategies

In this report we review memory-based meta-learning as a tool for buildi...

16 Pedro A. Ortega, et al. ∙

∙ 10/02/2018

Sinkhorn AutoEncoders

Optimal Transport offers an alternative to maximum likelihood for learni...

6 Giorgio Patrini, et al. ∙

∙ 04/16/2018

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

Inspired by findings of sensorimotor coupling in humans and animals, the...

0 Zhen Peng, et al. ∙

∙ 02/14/2017

On Detecting Adversarial Perturbations

Machine learning and deep learning in particular has advanced tremendous...

0 Jan-Hendrik Metzen, et al. ∙

∙ 04/07/2016

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

Information-theoretic principles for learning and acting have been propo...

0 Jordi Grau-Moya, et al. ∙

∙ 12/16/2013

Abstraction in decision-makers with limited information processing capabilities

A distinctive property of human and animal intelligence is the ability t...

0 Tim Genewein, et al. ∙

∙ 06/09/2012

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

We propose a novel Bayesian approach to solve stochastic optimization pr...

0 Pedro A. Ortega, et al. ∙