Search Results for author: Howard Bowman

Found 10 papers, 0 papers with code

Reframing the Expected Free Energy: Four Formulations and a Unification

no code implementations22 Feb 2024 Théophile Champion, Howard Bowman, Dimitrije Marković, Marek Grześ

In the second setting, a justification of the root expected free energy definition is known, but this setting only accounts for two formulations, i. e., the risk over states plus ambiguity and entropy plus expected energy formulations.

Decision Making

Predicting recovery following stroke: deep learning, multimodal data and feature selection using explainable AI

no code implementations29 Oct 2023 Adam White, Margarita Saranti, Artur d'Avila Garcez, Thomas M. H. Hope, Cathy J. Price, Howard Bowman

The highest classification accuracy 0. 854 was observed when 8 regions-of-interest was extracted from each MRI scan and combined with lesion size, initial severity and recovery time in a 2D Residual Neural Network. Our findings demonstrate how imaging and tabular data can be combined for high post-stroke classification accuracy, even when the dataset is small in machine learning terms.

feature selection Stroke Classification

Deconstructing deep active inference

no code implementations2 Mar 2023 Théophile Champion, Marek Grześ, Lisa Bonheme, Howard Bowman

The goal of this activity is to solve more complicated tasks using deep active inference.

Decision Making

Multi-Modal and Multi-Factor Branching Time Active Inference

no code implementations24 Jun 2022 Théophile Champion, Marek Grześ, Howard Bowman

Active inference is a state-of-the-art framework for modelling the brain that explains a wide range of mechanisms such as habit formation, dopaminergic discharge and curiosity.

Branching Time Active Inference with Bayesian Filtering

no code implementations14 Dec 2021 Théophile Champion, Marek Grześ, Howard Bowman

Its root can be found in Active Inference (Friston et al., 2016; Da Costa et al., 2020; Champion et al., 2021c), a neuroscientific framework widely used for brain modelling, as well as in Monte Carlo Tree Search (Browne et al., 2012), a method broadly applied in the Reinforcement Learning literature.

Branching Time Active Inference: empirical study and complexity class analysis

no code implementations22 Nov 2021 Théophile Champion, Howard Bowman, Marek Grześ

This was enabled by the development of a variational message passing approach to active inference, which enables compositional construction of Bayesian networks for active inference.

Branching Time Active Inference: the theory and its generality

no code implementations22 Nov 2021 Théophile Champion, Lancelot Da Costa, Howard Bowman, Marek Grześ

In this paper, we present an alternative framework that aims to unify tree search and active inference by casting planning as a structure learning problem.

Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker

no code implementations23 Apr 2021 Théophile Champion, Marek Grześ, Howard Bowman

Since, variational message passing is a well-defined methodology for deriving Bayesian belief update equations, this paper opens the door to advanced generative models for active inference.

Incandescent Bulb and LED Brake Lights:Novel Analysis of Reaction Times

no code implementations20 Oct 2020 Ramaswamy Palaniappan, Surej Mouli, Evangelina Fringi, Howard Bowman, Ian McLoughlin

BrakeAcc results also show that experienced subjects were quicker to respond to the activation of brake lights by releasing the accelerator pedal.

Neural-Symbolic Learning and Reasoning: A Survey and Interpretation

no code implementations10 Nov 2017 Tarek R. Besold, Artur d'Avila Garcez, Sebastian Bader, Howard Bowman, Pedro Domingos, Pascal Hitzler, Kai-Uwe Kuehnberger, Luis C. Lamb, Daniel Lowd, Priscila Machado Vieira Lima, Leo de Penning, Gadi Pinkas, Hoifung Poon, Gerson Zaverucha

Recent studies in cognitive science, artificial intelligence, and psychology have produced a number of cognitive models of reasoning, learning, and language that are underpinned by computation.

Philosophy

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