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deeplearningfortsf

深度学习以进行时间序列预测

diffusion-models-pytorch

Pytorch implementation of Diffusion Models (https://arxiv.org/pdf/2006.11239.pdf)

diral

Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V Communication

double-deep-q-learning-for-resource-allocation

Reproduce results of the research article "Deep Reinforcement Learning Based Resource Allocation for V2V Communications"

dqn-based-uav-3d_path_planer

Realization of UAV's Track Planning in 3D Urban Environment Based on Reinforcement Learning Algorithm(DQN)

drl-ec3

[JSAC 2018] Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach

drl_uav_cellularnet

Simulator developed for the mobility management of UAV base stations project featuring A3C

drlcache

Deep reinforcement learning-based replacement strategy for content caching.

dual_stage_attention_rnn

DARNN

easy-rl

强化学习中文教程，在线阅读地址：https://datawhalechina.github.io/easy-rl/

edgecachingrl

fiss

[CVPR2023] Federated Incremental Semantic Segmentation

flow-forecast

Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).

fun-rec

推荐系统入门教程，在线阅读地址：https://datawhalechina.github.io/fun-rec/

gcn-tffc

比较 TCN、GRU、GCN、TGCN、 TCN+GCN 在 交通流量预测方面的准确率效果。

gdc

Graph Diffusion Convolution, as proposed in "Diffusion Improves Graph Learning" (NeurIPS 2019)

github-slideshow

A robot powered training repository :robot:

graph-wavenet

graph wavenet

graphembeddingrecommendationsystem

Python based Graph Propagation algorithm, DeepWalk to evaluate and compare preference propagation algorithms in heterogeneous information networks from user item relation ship.

hello-world

informer2020

The GitHub repository for the paper "Informer" accepted by AAAI 2021.

informer_chinesenotes

intelligent-reflecting-surface-aided-multi-user-mmwave-communications-for-coverage-enhancement

PIMRC paper Reproducible codes

irs-continuous

Source code for "Intelligent Reflecting Surface Operation under Predictable Receiver Mobility: A Continuous Time Propagation Model" by Bho Matthiesen, Emil Björnson, Elisabeth De Carvalho, and Petar Popovski published in IEEE Wireless Communications Letters

irsconfigurationdrl

Source code for "Intelligent Reflecting Surface Configurations for Smart Radio Using Deep Reinforcement Learning", IEEE JSAC.

keras-io

Keras documentation, hosted live at keras.io

list_of_recommender_systems

A List of Recommender Systems and Resources

lite-deepsc

lstm

基于LSTM的时间序列预测研究

lstm-load-forecastin

Implementation of Electric Load Forecasting Based on LSTM(BiLSTM). Including Univariate-SingleStep forecasting, Multivariate-SingleStep forecasting and Multivariate-MultiStep forecasting.