Beren Millidge | DeepAI

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∙ 07/27/2023

Collective behavior from surprise minimization

Collective motion is ubiquitous in nature; groups of animals, such as fi...

0 Conor Heins, et al. ∙

∙ 06/27/2023

Causal Inference via Predictive Coding

Bayesian and causal inference are fundamental processes for intelligence...

0 Tommaso Salvatori, et al. ∙

∙ 12/02/2022

Designing Ecosystems of Intelligence from First Principles

This white paper lays out a vision of research and development in the fi...

0 Karl J. Friston, et al. ∙

∙ 11/22/2022

Interpreting Neural Networks through the Polytope Lens

Mechanistic interpretability aims to explain what a neural network has l...

0 Sid Black, et al. ∙

∙ 08/15/2022

Preventing Deterioration of Classification Accuracy in Predictive Coding Networks

Predictive Coding Networks (PCNs) aim to learn a generative model of the...

0 Paul F. Kinghorn, et al. ∙

∙ 07/20/2022

Successor Representation Active Inference

Recent work has uncovered close links between between classical reinforc...

0 Beren Millidge, et al. ∙

∙ 06/01/2022

A Theoretical Framework for Inference Learning

Backpropagation (BP) is the most successful and widely used algorithm in...

0 Nick Alonso, et al. ∙

∙ 04/05/2022

Hybrid Predictive Coding: Inferring, Fast and Slow

Predictive coding is an influential model of cortical neural activity. I...

0 Alexander Tschantz, et al. ∙

∙ 01/11/2022

pymdp: A Python library for active inference in discrete state spaces

Active inference is an account of cognition and behavior in complex syst...

69 Conor Heins, et al. ∙

∙ 12/03/2021

Active Inference in Robotics and Artificial Agents: Survey and Challenges

Active inference is a mathematical framework which originated in computa...

19 Pablo Lanillos, et al. ∙

∙ 09/02/2021

Habitual and Reflective Control in Hierarchical Predictive Coding

In cognitive science, behaviour is often separated into two types. Refle...

0 Paul F. Kinghorn, et al. ∙

∙ 08/30/2021

A Mathematical Walkthrough and Discussion of the Free Energy Principle

The Free-Energy-Principle (FEP) is an influential and controversial theo...

0 Beren Millidge, et al. ∙

∙ 07/27/2021

Predictive Coding: a Theoretical and Experimental Review

Predictive coding offers a potentially unifying account of cortical func...

0 Beren Millidge, et al. ∙

∙ 06/30/2021

Applications of the Free Energy Principle to Machine Learning and Neuroscience

In this PhD thesis, we explore and apply methods inspired by the free en...

0 Beren Millidge, et al. ∙

∙ 06/04/2021

Online reinforcement learning with sparse rewards through an active inference capsule

Intelligent agents must pursue their goals in complex environments with ...

0 Alejandro Daniel Noel, et al. ∙

∙ 06/03/2021

Towards a Mathematical Theory of Abstraction

While the utility of well-chosen abstractions for understanding and pred...

0 Beren Millidge, et al. ∙

∙ 03/11/2021

Understanding the origin of information-seeking exploration in probabilistic objectives for control

The exploration-exploitation trade-off is central to the description of ...

0 Beren Millidge, et al. ∙

∙ 02/19/2021

Neural Kalman Filtering

The Kalman filter is a fundamental filtering algorithm that fuses noisy ...

0 Beren Millidge, et al. ∙

∙ 10/13/2020

Investigating the Scalability and Biological Plausibility of the Activation Relaxation Algorithm

The recently proposed Activation Relaxation (AR) algorithm provides a si...

0 Beren Millidge, et al. ∙

∙ 10/02/2020

Relaxing the Constraints on Predictive Coding Models

Predictive coding is an influential theory of cortical function which po...

0 Beren Millidge, et al. ∙

∙ 09/11/2020

Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain

The backpropagation of error algorithm (backprop) has been instrumental ...

15 Beren Millidge, et al. ∙

∙ 07/11/2020

Control as Hybrid Inference

The field of reinforcement learning can be split into model-based and mo...

1 Alexander Tschantz, et al. ∙

∙ 06/23/2020

On the Relationship Between Active Inference and Control as Inference

Active Inference (AIF) is an emerging framework in the brain sciences wh...

0 Beren Millidge, et al. ∙

∙ 06/13/2020

Reinforcement Learning as Iterative and Amortised Inference

There are several ways to categorise reinforcement learning (RL) algorit...

0 Beren Millidge, et al. ∙

∙ 06/07/2020

Predictive Coding Approximates Backprop along Arbitrary Computation Graphs

Backpropagation of error (backprop) is a powerful algorithm for training...

0 Beren Millidge, et al. ∙

∙ 04/17/2020

Whence the Expected Free Energy?

The Expected Free Energy (EFE) is a central quantity in the theory of ac...

0 Beren Millidge, et al. ∙

∙ 02/28/2020

Reinforcement Learning through Active Inference

The central tenet of reinforcement learning (RL) is that agents seek to ...

0 Alexander Tschantz, et al. ∙

∙ 07/08/2019

Deep Active Inference as Variational Policy Gradients

Active Inference is a theory of action arising from neuroscience which c...

1 Beren Millidge, et al. ∙

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