Mingcong-Cao's starred repositories
EfficientNet-PyTorch
A PyTorch implementation of EfficientNet
pytorch-cifar
95.47% on CIFAR10 with PyTorch
pytorch-cifar100
Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)
Hands-on-RL
https://hrl.boyuai.com/
DRL-code-pytorch
Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.
multimodal-deep-learning
This repository contains various models targetting multimodal representation learning, multimodal fusion for downstream tasks such as multimodal sentiment analysis.
get-started-with-JAX
The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.
morl-baselines
Multi-Objective Reinforcement Learning algorithms implementations.
ResNet-for-Radio-Recognition
Implementation and improvement of "Over the Air Deep Learning Based Radio Signal Classification"
Deep-Learning-Based-Radio-Signal-Classification
Final Project for AI Wireless
MountainCar_DQN_RND
Playing Mountain-Car without reward engineering, by combining DQN and Random Network Distillation (RND)
mbrl-smdp-ode
PyTorch implementation of "Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs", NeurIPS 2020
pinplay-tools
A collection of C/C++ programs and Python scripts to be used in conjunction with Intel Software Development Emulator (Intel SDE, available externally separately). The purpose is to use record/replay functionality in SDE for program analysis.
PDMORL-Preference-Driven-Multi-Objective-Reinforcement-Learning-Algorithm
A novel preference-driven multi-objective reinforcement learning algorithm using a single policy network that covers the entire preference space in a given domain.