Convex-analysis

Convex-analysis's Stars

• opendilab/LMDrive

  [CVPR 2024] LMDrive: Closed-Loop End-to-End Driving with Large Language Models

  Language:Jupyter Notebook66555

• microsoft/LMOps

  General technology for enabling AI capabilities w/ LLMs and MLLMs

  Language:Python3.7k281

• infiniflow/ragflow

  RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.

  Language:Python22.4k2.2k

• Zhuzzq/EdgeFed-MARL-MEC

  Language:Python6616

• AI4Finance-Foundation/ElegantRL

  Massively Parallel Deep Reinforcement Learning. 🔥

  Language:Python3.7k851

• Farama-Foundation/Gymnasium

  An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)

  Language:Python7.3k817

• openai/gym

  A toolkit for developing and comparing reinforcement learning algorithms.

  Language:Python34.8k8.6k

• labmlai/annotated_deep_learning_paper_implementations

  🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

  Language:Python56.1k5.8k

• V2Xgithub/WiLabV2Xsim

  V2X simulator

  Language:MATLAB7832

• ai4ce/V2X-Sim

  [RA-L2022] V2X-Sim Dataset and Benchmark

  11314

• Lookingforcommit/5G_NR_V2X_simulator

  A project which uses SUMO traffic simulator to model communication between self-driving cars in 5G network

  Language:Python7

• uamughal/V2X-System-Level-Simulator

  This repository provides the System Level implementation of the V2X communication. It provides V2V sidelink communication using PC5 Interface as well as V2I Communication using Uu interface.

  Language:MATLAB61

• neucn/ipgw

  东北大学校园网关客户端

  Language:Go15335

• facebookresearch/stochastic_gradient_push

  Stochastic Gradient Push for Distributed Deep Learning

  Language:Python15737

• euphoria0-0/Active-Client-Selection-for-Communication-efficient-Federated-Learning

  Active Client Selection for Federated Learning

  Language:Python4113

• IBM/adaptive-federated-learning

  Code for paper "Adaptive Federated Learning in Resource Constrained Edge Computing Systems"

  Language:Python24682

• matze/mtheme

  A modern LaTeX Beamer theme

  Language:TeX6.4k846

• bitzj2015/DRL-Networking

  Research on incentive mechanism design in mobile crowdsensing and mobile edge computing by deep reinforcement learning approaches.

  Language:Python11429

• lx10077/fedavgpy

  On the Convergence of FedAvg on Non-IID Data

  Language:Python25071