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04/21/2023
How good are variational autoencoders at transfer learning?
Variational autoencoders (VAEs) are used for transfer learning across va...
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03/02/2023
Deconstructing deep active inference
Active inference is a theory of perception, learning and decision making...
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12/21/2022
Crowd Score: A Method for the Evaluation of Jokes using Large Language Model AI Voters as Judges
This paper presents the Crowd Score, a novel method to assess the funnin...
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09/26/2022
FONDUE: an algorithm to find the optimal dimensionality of the latent representations of variational autoencoders
When training a variational autoencoder (VAE) on a given dataset, determ...
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06/24/2022
Multi-Modal and Multi-Factor Branching Time Active Inference
Active inference is a state-of-the-art framework for modelling the brain...
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05/17/2022
How do Variational Autoencoders Learn? Insights from Representational Similarity
The ability of Variational Autoencoders (VAEs) to learn disentangled rep...
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12/14/2021
Branching Time Active Inference with Bayesian Filtering
Branching Time Active Inference (Champion et al., 2021b,a) is a framewor...
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11/22/2021
Branching Time Active Inference: empirical study and complexity class analysis
Active inference is a state-of-the-art framework for modelling the brain...
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11/22/2021
Branching Time Active Inference: the theory and its generality
Over the last 10 to 15 years, active inference has helped to explain var...
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09/26/2021
Be More Active! Understanding the Differences between Mean and Sampled Representations of Variational Autoencoders
The ability of Variational Autoencoders to learn disentangled representa...
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04/23/2021
Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker
Active inference is a state-of-the-art framework in neuroscience that of...
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06/20/2018
Reinforcement Learning using Augmented Neural Networks
Neural networks allow Q-learning reinforcement learning agents such as d...
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09/22/2017