3 citations · 5 across the 5 of their papers we have counts for
5 papers
JPC: Flexible Inference for Predictive Coding Networks in JAX
Francesco Innocenti, Paul Kinghorn, Will Yun-Farmbrough +3
We introduce JPC, a JAX library for training neural networks with Predictive Coding. JPC provides a simple, fast and flexible interface to train a variety of PC networks (PCNs) inc…
Hybrid Recurrent Models Support Emergent Descriptions for Hierarchical Planning and Control
Poppy Collis, Ryan Singh, Paul F Kinghorn +1
An open problem in artificial intelligence is how systems can flexibly learn discrete abstractions that are useful for solving inherently continuous problems. Previous work has dem…
Modeling Sustainable Resource Management using Active Inference
Mahault Albarracin, Ines Hipolito, Maria Raffa +1
Active inference helps us simulate adaptive behavior and decision-making in biological and artificial agents. Building on our previous work exploring the relationship between activ…
Understanding Tool Discovery and Tool Innovation Using Active Inference
Poppy Collis, Paul F Kinghorn, Christopher L Buckley
The ability to invent new tools has been identified as an important facet of our ability as a species to problem solve in dynamic and novel environments. While the use of tools by…
Preventing Deterioration of Classification Accuracy in Predictive Coding Networks
Paul F Kinghorn, Beren Millidge, Christopher L Buckley
Predictive Coding Networks (PCNs) aim to learn a generative model of the world. Given observations, this generative model can then be inverted to infer the causes of those observat…