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Found 27 papers, 9 papers with code
JPC: Flexible Inference for Predictive Coding Networks in JAX
We introduce JPC, a JAX library for training neural networks with Predictive Coding.
Variational Bayes Gaussian Splatting
Recently, 3D Gaussian Splatting has emerged as a promising approach for modeling 3D scenes using mixtures of Gaussians.
R-AIF: Solving Sparse-Reward Robotic Tasks from Pixels with Active Inference and World Models
Although research has produced promising results demonstrating the utility of active inference (AIF) in Markov decision processes (MDPs), there is relatively less work that builds AIF models in the context of environments and problems that take the form of partially observable Markov decision processes (POMDPs).
Exploring Action-Centric Representations Through the Lens of Rate-Distortion Theory
The efficient coding hypothesis claims that organisms seek to maximize the information about the sensory input in an efficient manner.
Only Strict Saddles in the Energy Landscape of Predictive Coding Networks?
Based on these and other results, we conjecture that all the saddles of the equilibrated energy are strict.
Active Inference and Intentional Behaviour
First, we simulate the aforementioned in vitro experiments, in which neuronal cultures spontaneously learn to play Pong, by implementing nested, free energy minimising processes.
Relative representations for cognitive graphs
Although the latent spaces learned by distinct neural networks are not generally directly comparable, recent work in machine learning has shown that it is possible to use the similarities and differences among latent space vectors to derive "relative representations" with comparable representational power to their "absolute" counterparts, and which are nearly identical across models trained on similar data distributions.
A Survey on Brain-Inspired Deep Learning via Predictive Coding
Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century.
Understanding Predictive Coding as an Adaptive Trust-Region Method
Predictive coding (PC) is a brain-inspired local learning algorithm that has recently been suggested to provide advantages over backpropagation (BP) in biologically relevant scenarios.
Attention: Marginal Probability is All You Need?
Recently attentional mechanisms have become a dominating architectural choice of machine learning and are the central innovation of Transformers.
Pretraining Language Models with Human Preferences
Language models (LMs) are pretrained to imitate internet text, including content that would violate human preferences if generated by an LM: falsehoods, offensive comments, personally identifiable information, low-quality or buggy code, and more.
Capsule Networks as Generative Models
Capsule networks are a neural network architecture specialized for visual scene recognition.
Knitting a Markov blanket is hard when you are out-of-equilibrium: two examples in canonical nonequilibrium models
Bayesian theories of biological and brain function speculate that Markov blankets (a conditional independence separating a system from external states) play a key role for facilitating inference-like behaviour in living systems.
Active Inference in Robotics and Artificial Agents: Survey and Challenges
Active inference is a mathematical framework which originated in computational neuroscience as a theory of how the brain implements action, perception and learning.
Habitual and Reflective Control in Hierarchical Predictive Coding
In cognitive science, behaviour is often separated into two types.
How particular is the physics of the free energy principle?
We discover that two requirements of the FEP -- the Markov blanket condition (i. e. a statistical boundary precluding direct coupling between internal and external states) and stringent restrictions on its solenoidal flows (i. e. tendencies driving a system out of equilibrium) -- are only valid for a very narrow space of parameters.
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.
Predictive Coding Approximates Backprop along Arbitrary Computation Graphs
Recently, it has been shown that backprop in multilayer-perceptrons (MLPs) can be approximated using predictive coding, a biologically-plausible process theory of cortical computation which relies only on local and Hebbian updates.
Whence the Expected Free Energy?
The Expected Free Energy (EFE) is a central quantity in the theory of active inference.
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.
Generative models as parsimonious descriptions of sensorimotor loops
The Bayesian brain hypothesis, predictive processing and variational free energy minimisation are typically used to describe perceptual processes based on accurate generative models of the world.
Nonmodular architectures of cognitive systems based on active inference
We link this to popular formulations of perception and action in the cognitive sciences, and show its limitations when, for instance, external forces are not modelled by an agent.
A Minimal Active Inference Agent
Research on the so-called "free-energy principle'' (FEP) in cognitive neuroscience is becoming increasingly high-profile.
