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Found 11 papers, 2 papers with code
Supervised structure learning
This paper concerns structure learning or discovery of discrete generative models.
Understanding Tool Discovery and Tool Innovation Using Active Inference
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.
Designing Ecosystems of Intelligence from First Principles
In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one's sensed world -- also known as self-evidencing.
Preventing Deterioration of Classification Accuracy in Predictive Coding Networks
Predictive Coding Networks (PCNs) aim to learn a generative model of the world.
Successor Representation Active Inference
Recent work has uncovered close links between between classical reinforcement learning algorithms, Bayesian filtering, and Active Inference which lets us understand value functions in terms of Bayesian posteriors.
RL with KL penalties is better viewed as Bayesian inference
We show that KL-regularised RL is equivalent to variational inference: approximating a Bayesian posterior which specifies how to update a prior LM to conform with evidence provided by the reward function.
Hybrid Predictive Coding: Inferring, Fast and Slow
This is at odds with evidence that several aspects of visual perception - including complex forms of object recognition - arise from an initial "feedforward sweep" that occurs on fast timescales which preclude substantial recurrent activity.
A Mathematical Walkthrough and Discussion of the Free Energy Principle
The Free-Energy-Principle (FEP) is an influential and controversial theory which postulates a deep and powerful connection between the stochastic thermodynamics of self-organization and learning through variational inference.
Predictive Coding: a Theoretical and Experimental Review
Predictive coding offers a potentially unifying account of cortical function -- postulating that the core function of the brain is to minimize prediction errors with respect to a generative model of the world.
Investigating the Scalability and Biological Plausibility of the Activation Relaxation Algorithm
The recently proposed Activation Relaxation (AR) algorithm provides a simple and robust approach for approximating the backpropagation of error algorithm using only local learning rules.
Relaxing the Constraints on Predictive Coding Models
Predictive coding is an influential theory of cortical function which posits that the principal computation the brain performs, which underlies both perception and learning, is the minimization of prediction errors.
