Bayesian Deep Learning: A Survey

There are C language computer programs about the simulator, transformation, and test statistic of continuous Bernoulli distribution. More than that, the book contains continuous Binomial distribution and continuous Trinomial distribution.

A Collection of Variational Autoencoders (VAE) in PyTorch.

GANs, AEs, and VAEs for generating synthetic images

Stellar spectra-driven latent space sonification using a variational autoencoder. 3D Sound spatialization in 360 degrees.

🌟 Welcome to the Machine Learning and Deep Learning Projects repository! This project is a compilation of diverse and engaging projects spanning computer vision, Kaggle competitions, generative AI, and advanced techniques such as autoencoders and variational autoencoders

Deep and Machine Learning for Microscopy

Unsupervised speech enhancement using DVAEs

This GitHub repository provides a structured path for going from beginner to advanced in data science, machine learning, and specifically - generative AI.

The project explores a range of methods, including both statistical analysis, traditional machine learning and deep learning approaches to anomaly detection a critical aspect of data science and machine learning, with a specific application to the detection of credit card fraud detection and prevention.

Easy generative modeling in PyTorch.

An experimental deep learning & genotype network-based system for predicting new influenza protein sequences.

Deep Learning homeworks (UniPD)

Research on Material Science using Neural Networks black box approach

Repository for Deep Structural Causal Models for Tractable Counterfactual Inference

People with pulmonary disease often have a high opacity, which makes segmentation of the lung from chest X-rays more difficult. In this study, I propose a methodology to improve the performance of the U-NET structure so that it is able to extract the features and spatial characteristics of the X-ray images of the chest region.

The study relied on conditional Variational Autoencoders to generate x-ray images, so that we can be able to regenerate the images according to the most important information that the x-ray images can contain (important information extraction).

Final project for the course “Laboratory of Computational Physics” from the MSc in Physics of Data

Using VAEs and GANs to understand how to generate images, over the CelebA dataset

Code for the Paper: "Conditional Variational Capsule Network for Open Set Recognition", Y. Guo, G. Camporese, W. Yang, A. Sperduti, L. Ballan, ICCV, 2021.