tao zhou's repositories
2017-learning-to-run
The Winning Solution for the Learning To Run Challenge 2017
ActiveRagdollStyleTransfer
Research into locomotion style transfer with Active Ragdolls (using MarathonEnvs +ml_agents)
AI-interview-cards
最完整的AI算法面试题目仓库，1000道，25个类目
algorithm-pattern-python
Python version of algorithm-pattern
CG
computer graphics homework
clip-italian
CLIP (Contrastive Language–Image Pre-training) for Italian
clip-multilingual
Multilingual CLIP - Semantic Image Search in 100 languages
DeepMVS
DeepMVS: Learning Multi-View Stereopsis
DQN-tensorflow
Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning
github-slideshow
A robot powered training repository :robot:
KELIP
Official PyTorch implementation of "Large-scale Bilingual Language-Image Contrastive Learning" (ICLRW 2022)
models
Models built with TensorFlow
Multilingual-CLIP
OpenAI CLIP text encoders for multiple languages!
NLP_Crawler1
Crawled professors' info and developed a parser based on Stanford open source tools
NoisyNet-A3C
Noisy Networks for Exploration
NoisyNet-DQN
Tensorflow Implementation for "Noisy network for exploration"
osim-rl
Reinforcement learning environments with musculoskeletal models
planets
planets in milky way
reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
Run-Skeleton-Run
Reason8.ai PyTorch solution for NIPS RL 2017 challenge
SuperGlue-pytorch
[SuperGlue: Learning Feature Matching with Graph Neural Networks] This repo includes PyTorch code for training the SuperGlue matching network on top of SIFT keypoints and descriptors.
SuperGluePretrainedNetwork
SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
tensorpack
Neural Network Toolbox on TensorFlow
UCLA-CS-32
These are my solutions for the four projects and five homeworks from UCLA CS 32 Spring 2015 with Prof Smallberg. These are my own solutions and are therefore not perfect. The source code for the various projects should only be used as a vague guideline to help you if you are stuck. Do not copy directly from these files as they will result in your own penalisation!