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Found 8 papers, 3 papers with code
Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain
The backpropagation of error algorithm (backprop) has been instrumental in the recent success of deep learning.
Control as Hybrid Inference
The field of reinforcement learning can be split into model-based and model-free methods.
On the Relationship Between Active Inference and Control as Inference
Active Inference (AIF) is an emerging framework in the brain sciences which suggests that biological agents act to minimise a variational bound on model evidence.
Reinforcement Learning as Iterative and Amortised Inference
There are several ways to categorise reinforcement learning (RL) algorithms, such as either model-based or model-free, policy-based or planning-based, on-policy or off-policy, and online or offline.
Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data
The broad concept of emergence is instrumental in various of the most challenging open scientific questions -- yet, few quantitative theories of what constitutes emergent phenomena have been proposed.
Reinforcement Learning through Active Inference
The central tenet of reinforcement learning (RL) is that agents seek to maximize the sum of cumulative rewards.
Scaling active inference
In reinforcement learning (RL), agents often operate in partially observed and uncertain environments.
Beyond integrated information: A taxonomy of information dynamics phenomena
Most information dynamics and statistical causal analysis frameworks rely on the common intuition that causal interactions are intrinsically pairwise -- every 'cause' variable has an associated 'effect' variable, so that a 'causal arrow' can be drawn between them.
Neurons and Cognition Data Analysis, Statistics and Probability
