Generative ModelsAnnotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
Stars: ✭ 438 (+250.4%)
MojitalkCode for "MojiTalk: Generating Emotional Responses at Scale" https://arxiv.org/abs/1711.04090
Stars: ✭ 107 (-14.4%)
Robotics Rl SrlS-RL Toolbox: Reinforcement Learning (RL) and State Representation Learning (SRL) for Robotics
Stars: ✭ 453 (+262.4%)
Link PredictionRepresentation learning for link prediction within social networks
Stars: ✭ 245 (+96%)
Tensorflow BookAccompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
Stars: ✭ 4,448 (+3458.4%)
Numpy MlMachine learning, in numpy
Stars: ✭ 11,100 (+8780%)
Pytorch RlThis repository contains model-free deep reinforcement learning algorithms implemented in Pytorch
Stars: ✭ 394 (+215.2%)
probabilistic nlgTensorflow Implementation of Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation (NAACL 2019).
Stars: ✭ 28 (-77.6%)
Awesome VaesA curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
Stars: ✭ 418 (+234.4%)
KateCode & data accompanying the KDD 2017 paper "KATE: K-Competitive Autoencoder for Text"
Stars: ✭ 135 (+8%)
disent🧶 Modular VAE disentanglement framework for python built with PyTorch Lightning ▸ Including metrics and datasets ▸ With strongly supervised, weakly supervised and unsupervised methods ▸ Easily configured and run with Hydra config ▸ Inspired by disentanglement_lib
Stars: ✭ 41 (-67.2%)
Pytorch sac aePyTorch implementation of Soft Actor-Critic + Autoencoder(SAC+AE)
Stars: ✭ 94 (-24.8%)
Tensorflow Mnist CvaeTensorflow implementation of conditional variational auto-encoder for MNIST
Stars: ✭ 139 (+11.2%)
tensorflow-mnist-AAETensorflow implementation of adversarial auto-encoder for MNIST
Stars: ✭ 86 (-31.2%)
Disentangling VaeExperiments for understanding disentanglement in VAE latent representations
Stars: ✭ 398 (+218.4%)
Awesome TensorlayerA curated list of dedicated resources and applications
Stars: ✭ 248 (+98.4%)
DESOM🌐 Deep Embedded Self-Organizing Map: Joint Representation Learning and Self-Organization
Stars: ✭ 76 (-39.2%)
srVAEVAE with RealNVP prior and Super-Resolution VAE in PyTorch. Code release for https://arxiv.org/abs/2006.05218.
Stars: ✭ 56 (-55.2%)
autoencoders tensorflowAutomatic feature engineering using deep learning and Bayesian inference using TensorFlow.
Stars: ✭ 66 (-47.2%)
SmrtHandle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.
Stars: ✭ 102 (-18.4%)
Tensorflow Mnist VaeTensorflow implementation of variational auto-encoder for MNIST
Stars: ✭ 422 (+237.6%)
CodeslamImplementation of CodeSLAM — Learning a Compact, Optimisable Representation for Dense Visual SLAM paper (https://arxiv.org/pdf/1804.00874.pdf)
Stars: ✭ 64 (-48.8%)
Pytorch cppDeep Learning sample programs using PyTorch in C++
Stars: ✭ 114 (-8.8%)
Doom Net PytorchReinforcement learning models in ViZDoom environment
Stars: ✭ 113 (-9.6%)
Startcraft pysc2 minigamesStartcraft II Machine Learning research with DeepMind pysc2 python library .mini-games and agents.
Stars: ✭ 113 (-9.6%)
Sigver wiwdLearned representation for Offline Handwritten Signature Verification. Models and code to extract features from signature images.
Stars: ✭ 112 (-10.4%)
MultiagenttorcsThe multi-agent version of TORCS for developing control algorithms for fully autonomous driving in the cluttered, multi-agent settings of everyday life.
Stars: ✭ 122 (-2.4%)
Machine learning lecturesCollection of lectures and lab lectures on machine learning and deep learning. Lab practices in Python and TensorFlow.
Stars: ✭ 118 (-5.6%)
StudybookStudy E-Book(ComputerVision DeepLearning MachineLearning Math NLP Python ReinforcementLearning)
Stars: ✭ 1,457 (+1065.6%)
Ctc ExecutionerMaster Thesis: Limit order placement with Reinforcement Learning
Stars: ✭ 112 (-10.4%)
Reinforcementlearning AtarigamePytorch LSTM RNN for reinforcement learning to play Atari games from OpenAI Universe. We also use Google Deep Mind's Asynchronous Advantage Actor-Critic (A3C) Algorithm. This is much superior and efficient than DQN and obsoletes it. Can play on many games
Stars: ✭ 118 (-5.6%)
GpndGenerative Probabilistic Novelty Detection with Adversarial Autoencoders
Stars: ✭ 112 (-10.4%)
Handful Of Trials PytorchUnofficial Pytorch code for "Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models"
Stars: ✭ 112 (-10.4%)
Rl MedicalDeep Reinforcement Learning (DRL) agents applied to medical images
Stars: ✭ 123 (-1.6%)
Vae TensorflowA Tensorflow implementation of a Variational Autoencoder for the deep learning course at the University of Southern California (USC).
Stars: ✭ 117 (-6.4%)
NavbotUsing RGB Image as Visual Input for Mapless Robot Navigation
Stars: ✭ 111 (-11.2%)
AmpligraphPython library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org
Stars: ✭ 1,662 (+1229.6%)
Pytorch RlTutorials for reinforcement learning in PyTorch and Gym by implementing a few of the popular algorithms. [IN PROGRESS]
Stars: ✭ 121 (-3.2%)
Pairstrade Fyp 2019We tested 3 approaches for Pair Trading: distance, cointegration and reinforcement learning approach.
Stars: ✭ 109 (-12.8%)
O GanO-GAN: Extremely Concise Approach for Auto-Encoding Generative Adversarial Networks
Stars: ✭ 117 (-6.4%)
CartpoleOpenAI's cartpole env solver.
Stars: ✭ 107 (-14.4%)
Lang Emerge ParlaiImplementation of EMNLP 2017 Paper "Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog" using PyTorch and ParlAI
Stars: ✭ 106 (-15.2%)
PclPyTorch code for "Prototypical Contrastive Learning of Unsupervised Representations"
Stars: ✭ 124 (-0.8%)
Multi object datasetsMulti-object image datasets with ground-truth segmentation masks and generative factors.
Stars: ✭ 121 (-3.2%)
Easy Rl强化学习中文教程,在线阅读地址:https://datawhalechina.github.io/easy-rl/
Stars: ✭ 3,004 (+2303.2%)
Cross Lingual Voice CloningTacotron 2 - PyTorch implementation with faster-than-realtime inference modified to enable cross lingual voice cloning.
Stars: ✭ 106 (-15.2%)
DeepaiDetection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.
Stars: ✭ 104 (-16.8%)