119 citations · 515 across the 60 of their papers we have counts for
9 papers · 1 filter
Investigating the Scalability and Biological Plausibility of the Activation Relaxation Algorithm
Beren Millidge, Alexander Tschantz, Anil Seth +1
The recently proposed Activation Relaxation (AR) algorithm provides a simple and robust approach for approximating the backpropagation of error algorithm using only local learning…
Relaxing the Constraints on Predictive Coding Models
Beren Millidge, Alexander Tschantz, Anil Seth +1
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…
Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain
Beren Millidge, Alexander Tschantz, Anil K Seth +1
The backpropagation of error algorithm (backprop) has been instrumental in the recent success of deep learning. However, a key question remains as to whether backprop can be formul…
Control as Hybrid Inference
Alexander Tschantz, Beren Millidge, Anil K. Seth +1
The field of reinforcement learning can be split into model-based and model-free methods. Here, we unify these approaches by casting model-free policy optimisation as amortised var…
Reinforcement Learning as Iterative and Amortised Inference
Beren Millidge, Alexander Tschantz, Anil K Seth +1
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…
On the Relationship Between Active Inference and Control as Inference
Beren Millidge, Alexander Tschantz, Anil K Seth +1
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. Control-as-Infere…
Predictive Coding Approximates Backprop along Arbitrary Computation Graphs
Beren Millidge, Alexander Tschantz, Christopher L. Buckley
Backpropagation of error (backprop) is a powerful algorithm for training machine learning architectures through end-to-end differentiation. However, backprop is often criticised fo…
Whence the Expected Free Energy?
Beren Millidge, Alexander Tschantz, Christopher L Buckley
The Expected Free Energy (EFE) is a central quantity in the theory of active inference. It is the quantity that all active inference agents are mandated to minimize through action,…
Reinforcement Learning through Active Inference
Alexander Tschantz, Beren Millidge, Anil K. Seth +1
The central tenet of reinforcement learning (RL) is that agents seek to maximize the sum of cumulative rewards. In contrast, active inference, an emerging framework within cognitiv…