96 Open source projects that are alternatives of or similar to Dropouts

A collection of Methods and Models for various architectures of Artificial Neural Networks

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

Natural Gradient, Variational Inference

Code for the paper Implicit Weight Uncertainty in Neural Networks

Reusable, Easy-to-use Uncertainty module package built with Tensorflow, Keras

MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.

Stochastic Delta Rule implemented in Pytorch on DenseNet

Google Street View House Number(SVHN) Dataset, and classifying them through CNN

[ACM MM 2020] Uncertainty-based Traffic Accident Anticipation

AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics

Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学

Complementary code for the Targeted Dropout paper

Pytorch implementation of Variational Dropout Sparsifies Deep Neural Networks

datagrand 2019 information extraction competition rank9

Selective Classification For Deep Neural Networks.

The tools and syntax you need to code neural networks from day one.

Abstractive opinion summarization system (SelSum) and the largest dataset of Amazon product summaries (AmaSum). EMNLP 2021 conference paper.

Satania IS the BEST waifu, no really, she is, if you don't believe me, this website will convince you

Implementation of DropBlock: A regularization method for convolutional networks in PyTorch.

A Tensorflow implementation of "Bayesian Graph Convolutional Neural Networks" (AAAI 2019).

Implementation of CNNs, RNNs, and many deep learning techniques in plain Numpy.

My solutions for Assignments of CS231n: Convolutional Neural Networks for Visual Recognition

Statistical Rethinking (2nd ed.) with NumPyro

Win probability predictions for League of Legends matches using neural networks

Demultiplexing pooled scRNA-seq data with or without genotype reference

A list of awesome resources on normalizing flows.

Imputation method for scRNA-seq based on low-rank approximation

A toy model of Friston's active inference in Tensorflow

Artificial Intelligence Learning Notes.

DGMs for NLP. A roadmap.

repo that holds code for improving on dropout using Stochastic Delta Rule

A Latent Dirichlet Allocation implementation in Python.

PyTorch implementations of LSTM Variants (Dropout + Layer Norm)

A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions

Single (i) Cell R package (iCellR) is an interactive R package to work with high-throughput single cell sequencing technologies (i.e scRNA-seq, scVDJ-seq, ST and CITE-seq).

A tensorflow implementation of the NIPS 2018 paper "Variational Inference with Tail-adaptive f-Divergence"

Complex-valued neural networks for pytorch and Variational Dropout for real and complex layers.

Repository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen

Variance Networks: When Expectation Does Not Meet Your Expectations, ICLR 2019

Regression with Summary Statistics.

Learning about Haskell with Variational Autoencoders

Using neural network to build expressive hierarchical distribution; A variational method to accurately estimate posterior uncertainty; A fast and general method for Bayesian inference. (ICML 2018)

Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)

🔬 Nano size Theano LSTM module

DropClass and DropAdapt - repository for the paper accepted to Speaker Odyssey 2020

Scalable inference for a generative model of astronomical images

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

Bayesian mixture models for estimating and clustering cancer cell fractions

Collection of probabilistic models and inference algorithms

Bayesian neural networks in PyTorch

Implementation of VLAE

PyTorch implementation of normalizing flow models

Statistical Rethinking (2nd Ed) with Tensorflow Probability

Julia package for automatic Bayesian inference on a factor graph with reactive message passing

(ICML 2022) Official PyTorch implementation of “Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness”.

Deep active inference agents using Monte-Carlo methods

My workshop on machine learning using python language to implement different algorithms

Repo for the Tutorials of Day1-Day3 of the Nordic Probabilistic AI School 2021 (https://probabilistic.ai/)

Implementation of key concepts of neuralnetwork via numpy

Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features