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adjoint-maml

Repository of Meta-Learning with Adjoint Methods

adversarialvariationalbayes

This repository contains the code to reproduce the core results from the paper "Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks".

bayesian-neural-networks

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

bmbo-darn

Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks

bmfal-bc

Repository of Batch Multi-Fidelity Active Learning with Budget Constraints

cs224n

Stanford NLP class homework

cs6350-pytorch-tutorial

darts

Differentiable architecture search for convolutional and recurrent networks

dmfal

dnn-mfbo

Multi-fidelity Bayesian Optimization via Deep Neural Nets

dpboost

Privacy-Preserving Gradient Boosting Decision Trees (AAAI 2020)

envs

examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

exputils

gp-parallel-ts

Parallelised Thompson Sampling in GPs for Bayesian Optimisation

imaml_dev

Repo to accompany paper on Meta Learning with Implicit Gradients (NeurIPS 2019)

infinite-fidelity-coregionalization

Repository of Infinite-Fidelity Coregionalization for Physical Simulation

jekyll-course-website-template

Feature-rich and easy-to-use Jekyll website template for academic courses

lightddpm

light implementation of diffusion models

maml-pytorch

Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)

metalic

mra-fno

neural-flows-experiments

Experiments for Neural Flows paper

prml

PRML algorithms implemented in Python

pytorch-book

PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》)

pytorch-tutorial

PyTorch Tutorial for Deep Learning Researchers

scalable-gprn

Scalable Gaussian Process Regression Networks

shib0li.github.io

shiboli_github_io_old

tensorflow-examples

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)