research
∙
09/19/2023
Language Modeling Is Compression
It has long been established that predictive models can be transformed i...
share
research
∙
05/26/2023
Randomized Positional Encodings Boost Length Generalization of Transformers
Transformers have impressive generalization capabilities on tasks with a...
research
∙
02/06/2023
Memory-Based Meta-Learning on Non-Stationary Distributions
Memory-based meta-learning is a technique for approximating Bayes-optima...
research
∙
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...
research
∙
07/05/2022
Neural Networks and the Chomsky Hierarchy
Reliable generalization lies at the heart of safe ML and AI. However, un...
research
∙
03/23/2022
Your Policy Regularizer is Secretly an Adversary
Policy regularization methods such as maximum entropy regularization are...
research
∙
11/04/2021
Model-Free Risk-Sensitive Reinforcement Learning
We extend temporal-difference (TD) learning in order to obtain risk-sens...
research
∙
10/20/2021
Shaking the foundations: delusions in sequence models for interaction and control
The recent phenomenal success of language models has reinvigorated machi...
research
∙
03/05/2021
Causal Analysis of Agent Behavior for AI Safety
As machine learning systems become more powerful they also become increa...
research
∙
10/23/2020
Algorithms for Causal Reasoning in Probability Trees
Probability trees are one of the simplest models of causal generative pr...
research
∙
10/21/2020
Meta-trained agents implement Bayes-optimal agents
Memory-based meta-learning is a powerful technique to build agents that ...
research
∙
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...
research
∙
05/08/2019
Meta-learning of Sequential Strategies
In this report we review memory-based meta-learning as a tool for buildi...
research
∙
10/02/2018
Sinkhorn AutoEncoders
Optimal Transport offers an alternative to maximum likelihood for learni...
research
∙
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...
research
∙
02/14/2017
On Detecting Adversarial Perturbations
Machine learning and deep learning in particular has advanced tremendous...
research
∙
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...
research
∙
12/16/2013
Abstraction in decision-makers with limited information processing capabilities
A distinctive property of human and animal intelligence is the ability t...
research
∙
06/09/2012