Neural Network Many-Body Wavefunction Reconstruction
-
Updated
Oct 9, 2024 - Python
Neural Network Many-Body Wavefunction Reconstruction
Implementation of G. E. Hinton and R. R. Salakhutdinov's Reducing the Dimensionality of Data with Neural Networks (Tensorflow)
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
A Python-implemented RBM project exploring generative learning through the classification of the Iris dataset, featuring a user-friendly GUI and advanced data handling capabilities.
Deep Belief Networks in Tensorflow 2
The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation net…
Keras framework for unsupervised learning
Energy Based Models in PyTorch
Demonstration of the mini-lab (practical) component activities conducted for the course of Neural Networks and Deep Learning (19CSE456).
Restricted Boltzmann Machine (classification on MNIST)
An pytorch implementation of Deep Belief Network with sklearn compatibility for classification. The training process consists the pretraining of DBN, fine-tuning as an unrolled autoencoder-decoder, and supervised fine-tuning as a classifier.
A restricted Boltzmann Machine trained using Persistent Contrastive Divergence implemented with Pytorch.
Project to generate novel Ising model spin states using Restricted Boltzmann Machines. Implemented using Python and SciKit-Learn. Coursework for course on Neural Networks. (MSc Computer Science level, Leiden University)
Numerically represent quantum states with Restricted Boltzmann Machines
Implementation of a gaussian restricted Blotzmann machine
hebbian learning for sequential generation
Always sparse. Never dense. But never say never. A Sparse Training repository for the Adaptive Sparse Connectivity concept and its algorithmic instantiation, i.e. Sparse Evolutionary Training, to boost Deep Learning scalability on various aspects (e.g. memory and computational time efficiency, representation and generalization power).
Mini Project for the "Machine Learning for Physicists 2020" course. A RBM implementation of a set of quantum mechanical harmonic oscillators.
Generative-Models (GAN, RBM)
Implementation of restricted Boltzmann machine, deep Boltzmann machine, deep belief network, and deep restricted Boltzmann network models using python. This code has some specalised features for 2D physics data.
Add a description, image, and links to the restricted-boltzmann-machine topic page so that developers can more easily learn about it.
To associate your repository with the restricted-boltzmann-machine topic, visit your repo's landing page and select "manage topics."