paper

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

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib.

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

Scan, index, and archive all of your paper documents

Code for the paper "Jukebox: A Generative Model for Music"

[CVPR2020] Adversarial Latent Autoencoders

Code for reproducing results in "Glow: Generative Flow with Invertible 1x1 Convolutions"

End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow

Image Deblurring using Generative Adversarial Networks

Code for the paper "Improved Techniques for Training GANs"

DeepMind's Tacotron-2 Tensorflow implementation

Code for the paper "PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications"

Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"

Code and model for the paper "Improving Language Understanding by Generative Pre-Training"

📈 目前最大的工业缺陷检测数据库及论文集 Constantly summarizing open source dataset and critical papers in the field of surface defect research which are of great importance.

Code for the paper "Evolution Strategies as a Scalable Alternative to Reinforcement Learning"

FSL-Mate: A collection of resources for few-shot learning (FSL).

Code for "Learning to Generate Reviews and Discovering Sentiment"

Code for the paper "Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents"