martinhavlicek Goto Github PK

adversarial

[For Fun - Complete] How to defend against adversarial inputs to deep nets.

adversarialvariationalbayes

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

amortized-variational-filtering

PyTorch implementation of AVF

animl

Reproduction of "Model-Agnostic Meta-Learning" (MAML) and "Reptile".

autoregressive-models

Tensorflow 2.0 implementation of Deep Autoregressive Models

bayesiandeeplearning

Overview of Bayesian Deep Learning

bayesloop

Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.

bigan

code for "Adversarial Feature Learning"

bigan_srl

Testing BIGAN (Adversarial Feature Learning) for State Representation Learning

bmaml

This repository contains implementations of the paper, Bayesian Model-Agnostic Meta-Learning.

bmaml_rl

This repository contains implementations of the paper, Bayesian Model-Agnostic Meta-Learning.

bpl

Bayesian Program Learning model for one-shot learning

char-rnn

Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch

contrastive-predictive-coding

Keras implementation of Representation Learning with Contrastive Predictive Coding

copy-move-detection-based-on-patchmatch

Implementation of a PatchMatch based image copy-move detection algorithm.

covid-19

Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE

ctgan

Conditional GAN for Tabular Data

dai

🧊 🚩Comparison of active inference, q-learning and bayesian rl using modified FrozenLake environment

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deep-q-learning-paper-to-code

deeprl-agents

A set of Deep Reinforcement Learning Agents implemented in Tensorflow.

extremevalueloss

Code implementing generative models using 'extreme value loss'.

fep

few_shot_meta_learning

Implementations of many meta-learning algorithms to solve the few-shot learning problem in Pytorch

finance_gan

Wasserstein GAN with gradient penalty (WGAN-GP) applied to financial time series.

gcn

Implementation of Graph Convolutional Networks in TensorFlow

generative-query-network-pytorch

Generative Query Network (GQN) in PyTorch as described in "Neural Scene Representation and Rendering"

go-explore

Code for Go-Explore: a New Approach for Hard-Exploration Problems

gpt-2

Code for the paper "Language Models are Unsupervised Multitask Learners"

gqn-datasets

Datasets used to train Generative Query Networks (GQNs) in the ‘Neural Scene Representation and Rendering’ paper.