@inproceedings{mnih2016asynchronous, title={Asynchronous methods for deep reinforcement learning}, author={Mnih, Volodymyr and Badia, Adria Puigdomenech and Mirza, Mehdi and Graves, Alex and Lillicrap, Timothy P and Harley, Tim and Silver, David and Kavukcuoglu, Koray}, booktitle={International Conference on Machine Learning}, year={2016}} This repository contains an implementation of Adavantage async Actor-Critic (A3C) in PyTorch based on the original paper by the authors and the PyTorch implementation by Ilya Kostrikov. A3C is the state-of-art Deep Reinforcement Learning method.    

This is a PyTorch implementation of the Transformer model in “Attention is All You Need” (Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin, arxiv, 2017). A novel sequence to sequence framework utilizes the self-attention mechanism, instead of Convolution operation or Recurrent structure, and achieve the state-of-the-art performance on WMT 2014 English-to-German translation task. (2017/06/12) The official Tensorflow Implementation can be found in: tensorflow/tensor2tensor. To learn more about self-attention mechanism, you could […]

This project is still in progress. If you are looking for the working code, use BEGAN-tensorflow. Requirements Usage First download CelebA datasets with: $ apt-get install p7zip-full # ubuntu $ brew install p7zip # Mac $ python download.py or you can use your own dataset by placing images like: data └── YOUR_DATASET_NAME ├── xxx.jpg (name doesn’t matter) ├── yyy.jpg └── … To train a model:

A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks If you’re interested in C++ inference, move HERE Results Steps GPU Batch Size Learning Rate Patience Decay Step Decay Rate Training Speed (FPS) Accuracy 54000 GTX 1080 Ti 512 0.16 100 625 0.9 ~1700 95.65% Sample $ python infer.py -c=./logs/model-54000.pth ./images/test-75.png length: 2 digits: 7 5 10 10 10

Code accompanying the paper “Wasserstein GAN” A few notes The first time running on the LSUN dataset it can take a long time (up to an hour) to create the dataloader. After the first run a small cache file will be created and the process should take a matter of seconds. The cache is a list of indices in the lmdb database (of LSUN) The only addition to the code (that we forgot, and will add, on the paper) are […]