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Found 20 papers, 6 papers with code
Associative Memories in the Feature Space
This problem can be easily solved by computing \emph{similarities} in an embedding space instead of the pixel space.
A Review of Neuroscience-Inspired Machine Learning
As a result, it accommodates hardware and scientific modeling, e. g. learning with physical systems and non-differentiable behavior.
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.
Supervised structure learning
This paper concerns structure learning or discovery of discrete generative models.
Brain-Inspired Computational Intelligence via Predictive Coding
Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century.
Causal Inference via Predictive Coding
Bayesian inference models observations: what can be inferred about y if we observe a related variable x?
Mathematical Capabilities of ChatGPT
We investigate the mathematical capabilities of two iterations of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel methodology.
Robust Graph Representation Learning via Predictive Coding
Predictive coding is a message-passing framework initially developed to model information processing in the brain, and now also topic of research in machine learning due to some interesting properties.
Graph Representation Learning
Out-of-Distribution Generalization
A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks
Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience.
Predictive Coding beyond Gaussian Distributions
A large amount of recent research has the far-reaching goal of finding training methods for deep neural networks that can serve as alternatives to backpropagation (BP).
Bird-Eye Transformers for Text Generation Models
Transformers have become an indispensable module for text generation models since their great success in machine translation.
A Theoretical Framework for Inference and Learning in Predictive Coding Networks
In this paper, we provide a comprehensive theoretical analysis of the properties of PCNs trained with prospective configuration.
Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learning
How the brain performs credit assignment is a fundamental unsolved problem in neuroscience.
Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation?
The backpropagation of error algorithm used to train deep neural networks has been fundamental to the successes of deep learning.
Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models
A large number of neural network models of associative memory have been proposed in the literature.
Learning on Arbitrary Graph Topologies via Predictive Coding
Training with backpropagation (BP) in standard deep learning consists of two main steps: a forward pass that maps a data point to its prediction, and a backward pass that propagates the error of this prediction back through the network.
Associative Memories via Predictive Coding
We conclude by discussing the possible impact of this work in the neuroscience community, by showing that our model provides a plausible framework to study learning and retrieval of memories in the brain, as it closely mimics the behavior of the hippocampus as a memory index and generative model.
Reverse Differentiation via Predictive Coding
Recent works prove that these methods can approximate BP up to a certain margin on multilayer perceptrons (MLPs), and asymptotically on any other complex model, and that zero-divergence inference learning (Z-IL), a variant of PC, is able to exactly implement BP on MLPs.
Predictive Coding Can Do Exact Backpropagation on Convolutional and Recurrent Neural Networks
Predictive coding networks (PCNs) are an influential model for information processing in the brain.
BoxE: A Box Embedding Model for Knowledge Base Completion
Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB).
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