zoey's starred repositories
HowToLiveLonger
程序员延寿指南 | A programmer's guide to live longer
External-Attention-pytorch
🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐
PyTorch_Tutorial
《Pytorch模型训练实用教程》中配套代码
Deep-reinforcement-learning-with-pytorch
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....
faceswap-GAN
A denoising autoencoder + adversarial losses and attention mechanisms for face swapping.
muzero-general
MuZero
Awesome-Knowledge-Distillation
Awesome Knowledge-Distillation. 分类整理的知识蒸馏paper(2014-2021)。
rainbow-is-all-you-need
Rainbow is all you need! A step-by-step tutorial from DQN to Rainbow
temperature_scaling
A simple way to calibrate your neural network.
DRL-code-pytorch
Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.
mdistiller
The official implementation of [CVPR2022] Decoupled Knowledge Distillation https://arxiv.org/abs/2203.08679 and [ICCV2023] DOT: A Distillation-Oriented Trainer https://openaccess.thecvf.com/content/ICCV2023/papers/Zhao_DOT_A_Distillation-Oriented_Trainer_ICCV_2023_paper.pdf
Safe-Reinforcement-Learning-Baselines
The repository is for safe reinforcement learning baselines.
calibration-framework
The net:cal calibration framework is a Python 3 library for measuring and mitigating miscalibration of uncertainty estimates, e.g., by a neural network.
NoisyNet-A3C
Noisy Networks for Exploration
LivePublisher
Android rtmp推流器
neurips2020-procgen-starter-kit
Starter Kit for NeurIPS 2020 - Procgen Competition on AIcrowd
5Gdataset
In this work, we present a 5G trace dataset collected from a major Irish mobile operator. The dataset is generated from two mobility patterns (static and car), and across two application patterns(video streaming and file download). The dataset is composed of client-side cellular key performance indicators (KPIs) comprised of channel-related metrics, context-related metrics, cell-related metrics and throughput information. These metrics are generated from a well-known non-rooted Android network monitoring application, G-NetTrack Pro. To the best of our knowledge, this is the first publicly available dataset that contains throughput, channel and context information for 5G networks. To supplement our real-time 5G production network dataset, we also provide a 5G large scale multi-cell ns-3 simulation framework. The availability of the 5G/mmwave module for the ns-3 mmwave network simulator provides an opportunity to improve our understanding of the dynamic reasoning for adaptive clients in 5G multi-cell wireless scenarios. The purpose of our framework is to provide additional information (such as competing metrics for users connected to the same cell), thus providing otherwise unavailable information about the basestation (eNodeB or eNB) environment and scheduling principle, to end user. Our framework permits other researchers to investigate this interaction through the generation of their own synthetic datasets.
Video-Streaming-Research-Papers
Research materials about multimedia network and system, including paper list, tools, etc.
Object-Detection-Confidence-Bias
Code for "The Box Size Confidence Bias Harms Your Object Detector" (https://arxiv.org/abs/2112.01901)
Pensieve-Pytorch
A Pytorch implementation of Pensieve (SIGCOMM'18)