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46 search results found

Bayesian Machine Learning ⭐ 1,587

Notebooks about Bayesian methods for machine learning

Variational Autoencoder ⭐ 1,114

Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)

Variational Autoencoder ⭐ 1,049

Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)

Bayesiandeeplearning Survey ⭐ 475

Bayesian Deep Learning: A Survey

Good Papers ⭐ 231

I try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers

S Vae Pytorch ⭐ 214

Pytorch implementation of Hyperspherical Variational Auto-Encoders

S Vae Tf ⭐ 178

Tensorflow implementation of Hyperspherical Variational Auto-Encoders

Generative And Discriminative Voxel Modeling ⭐ 170

Voxel-Based Variational Autoencoders, VAE GUI, and Convnets for Classification

Deep and Machine Learning for Microscopy

Numalogic ⭐ 156

Collection of operational time series ML models and tools

Fusilli ⭐ 120

A Python package housing a collection of deep-learning multi-modal data fusion method pipelines! From data loading, to training, to evaluation - fusilli's got you covered 🌸

Handle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.

Awesome Vqvae ⭐ 80

📚 A collection of resources and papers on Vector Quantized Variational Autoencoder (VQ-VAE) and its application

Contiguous Succotash ⭐ 70

Recurrent Variational Autoencoder with Dilated Convolutions that generates sequential data implemented in pytorch

Video_generator ⭐ 63

This is implementation of convolutional variational autoencoder in TensorFlow library and it will be used for video generation.

Pytorch implementation of stochastically quantized variational autoencoder (SQ-VAE)

Awesome Denovo Papers ⭐ 53

Awesome De novo drugs design papers

Multimodal Vae Public ⭐ 52

A PyTorch implementation of "Multimodal Generative Models for Scalable Weakly-Supervised Learning" (https://arxiv.org/abs/1802.05335)

Artificialintelligenceengines ⭐ 48

Computer code collated for use with Artificial Intelligence Engines book by JV Stone

Vae_protein_function ⭐ 44

Protein function prediction using a variational autoencoder

Source Filter Vae ⭐ 42

Learning and controlling the source-filter representation of speech with a variational autoencoder

scAR (single-cell Ambient Remover) is a deep learning model for removal of the ambient signals in droplet-based single cell omics

Mixed-curvature Variational Autoencoders (ICLR 2020)

Numpy Nn Model ⭐ 32

Сustom torch style machine learning framework with automatic differentiation implemented on numpy, allows build GANs, VAEs, etc.

Vae Anomaly Detector ⭐ 29

Experiments on unsupervised anomaly detection using variational autoencoder. The variational autoencoder is implemented in Pytorch.

Tensorflow_deep_learning_models ⭐ 23

TensorFlow implementations of several deep learning models (e.g. variational autoencoder, RNN, ...)

Disent And Gen ⭐ 20

Code for the article: The Role of Disentanglement in Generalisation (ICLR, 2021).

Molecular Vae ⭐ 19

Pytorch implementation of the paper "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules"

Optimisation Python ⭐ 18

A set of notebooks related to convex optimization, variational inference and numerical methods for signal processing, machine learning, deep learning, graph analysis, bayesian programming, statistics or astronomy.

Variationalautoencoders ⭐ 17

Comparison of Variational Autoencoders with Bayesian Neural Networks. Accuracy, Latent space, Reconstruction and White Noise filtering.

Simple Variational Autoencoder ⭐ 16

A VAE written entirely in Numpy/Cupy

Pirounet ⭐ 16

Conditional dance generator

An approach for embedding hierarhical structures into a continuous vector space using variational autoencoders.

Geometric Dynamic Variational Autoencoders (GD-VAEs) for learning embedding maps for nonlinear dynamics into general latent spaces. This includes methods for standard latent spaces or manifold latent spaces with specified geometry and topology. The manifold latent spaces can be based on analytic expressions or general point cloud representations.

Cellx Predict ⭐ 15

Explainable AI model of cell behavior

Orgainzed Digital Intelligent Network (O.D.I.N)

Vae_based_segmentation ⭐ 15

Exploring variational-autoencoder-based semantic segmentation for analyzing CT-scans.

Deep Blueberry ⭐ 14

If you've always wanted to learn about deep-learning but don't know where to start, then you might have stumbled upon the right place!

A probabilistic model to cluster survival data in a variational deep clustering setting

Generic Expression Patterns ⭐ 11

Distinguishing between generic and experiment-specific gene expression signals.

Deep and machine learning for atomic-scale and mesoscale data

Label Free Xai ⭐ 10

This repository contains the implementation of Label-Free XAI, a new framework to adapt explanation methods to unsupervised models. For more details, please read our ICML 2022 paper: 'Label-Free Explainability for Unsupervised Models'.

Official code for the "Optimal Variance Control of the Score Function Gradient Estimator for Importance Weighted Bounds"

Tss18 Robotsinmusicalimprovisation ⭐ 9

Robots in Musical Improvisation: Learning Algorithms Respecting Motion Constraints

Variational Auto Encoders for learning binding signatures of transcription factors

Bayesian Methods For Machine Learning ⭐ 8

Bayesian Methods for Machine Learning

Machine Learning Portfolio ⭐ 7

Assorted machine learning implementations for medical data

Accompany Music Vae ⭐ 7

Using Magenta's MusicVAE to generate musical accompaniment.

Multi-Channel Variational Auto Encoder: A Bayesian Deep Learning Framework for Modeling High-Dimensional Heterogeneous Data.

Tensorflow Advanced Techniques Solutions ⭐ 6

This repository contains my solutions for the Coursera course TensorFlow: Advanced Techniques Specialization. Expand your knowledge of the Functional API and build exotic non-sequential model types. Learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. Explore generative deep learning including the ways AIs can create new

Amld2020 Workshop ⭐ 6

This repo contains workshop material for Applied Machine Learning Days 2020 conference

Oneclassclassifier ⭐ 6

One-class classifiers for anomaly detection (outlier detection)

Pytorch_vae Gmm ⭐ 5

Implementation of a model to make VAE and GMM train from each other

(AIWAE) Improving importance weighted auto-encoders with annealed importance sampling

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