Reference implementation of "An Algorithm for Routing Vectors in Sequences" (Heinsen, 2022) and "An Algorithm for Routing Capsules in All Domains" (Heinsen, 2019), for composing deep neural networks.
- Updated
Apr 13, 2023 - Python
Reference implementation of "An Algorithm for Routing Vectors in Sequences" (Heinsen, 2022) and "An Algorithm for Routing Capsules in All Domains" (Heinsen, 2019), for composing deep neural networks.
Implementation of deep implicit attention in PyTorch
Holistic system for storage and transformation of information based on associative model of data. Целостная система для хранения и обработки информации, основанная на ассоциативной модели данных.
Physics-inspired transformer modules based on mean-field dynamics of vector-spin models in JAX
Cognitive Computing with Associative Memory
This repository contains the official code for Energy Transformer---an efficient Energy-based Transformer variant for graph classification
Implementation of Hopfield Neural Network in Python based on Hebbian Learning Algorithm
Recurrent predictive coding networks for associative memory employing covariance learning
[ICML 2024] Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models
PyTorch implementation of IJCAI 2020 paper Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network
Minimum Description Length Hopfield Networks
Open Source C++ Library for Pseudo-inverse Fully Connected Recurrent Neural Networks (from my PhD)
Source-codes and examples of quaternion-valued recurrent projection neural networks on unit quaternions
Simulation code for simulations run in my PhD
Publications by Peter Overmann
Secure your data with personal memories, not passwords
A neuro-inspired computational framework for modeling memory consolidation in neural networks, with applications from toy models to human connectomics.
This Repository contains scratch implementation of Soft Computing Lab practicals
hclust_mix is a Python script that allows the identification of attractor states from gene expression matrices using Hopfield neural networks.
Hopfield model for T=0 and finite T.
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