panyxy

panyxy's Stars

• huggingface/transformers

  🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

  Language:Python137k1.1k16.4k27.5k

• MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning

  This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."

  Language:MATLAB4.3k390565

• opendilab/awesome-RLHF

  A curated list of reinforcement learning with human feedback resources (continually updated)

  3.6k614220

• lm-sys/RouteLLM

  A framework for serving and evaluating LLM routers - save LLM costs without compromising quality

  Language:Python3.4k2553254

• paperswithcode/releasing-research-code

  Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)

  2.6k566717

• FeiLiu36/LLM4Opt

  A Collection on Large Language Models for Optimization

  1804521

• Edward-Sun/DIFUSCO

  Code of NeurIPS paper: arxiv.org/abs/2302.08224

  Language:Python17931340

• henry-yeh/DeepACO

  [NeurIPS 2023] DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization

  Language:Jupyter Notebook1374321

• martyput/MDP_book

  100817

• yining043/VRP-DACT

  This repo implements our paper, "Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer", which has been accepted at NeurIPS 2021.

  Language:Jupyter Notebook9421122

• ahottung/NLNS

  Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem

  Language:Python836030

• liuzuxin/DSRL

  🔥 Datasets and env wrappers for offline safe reinforcement learning

  Language:Python832125

• r-three/phatgoose

  Code for PHATGOOSE introduced in "Learning to Route Among Specialized Experts for Zero-Shot Generalization"

  Language:Python79166

• zdhNarsil/GFlowNet-CombOpt

  PyTorch implementation for our NeurIPS 2023 spotlight paper "Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets".

  Language:Python60259

• Not-Diamond/awesome-ai-model-routing

  A curated list of awesome approaches to AI model routing

  570010

• ahottung/EAS

  Efficient Active Search

  Language:Python48216

• Stalence/erdos_neu

  Official Repo for the NeurIPS2020 paper "Erdos Goes Neural: An Unsupervised Learning Framework for Combinatorial Optimization on Graphs"

  Language:Python453611

• yimengmin/UTSP

  code repo for paper Unsupervised Learning for Solving the Travelling Salesman Problem

  Language:C++44275

• DIMESTeam/DIMES

  Language:Python403106

• yining043/NeuOpt

  This repo implements our paper, "Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt", which has been accepted at NeurIPS 2023.

  Language:Jupyter Notebook39113

• marmotlab/DAN

  Public version of the decentralized, attention-based mTSP code

  Language:Python35107

• naver/bq-nco

  Language:Python35423

• Thinklab-SJTU/NAR-CO-Solver

  Official implementation non-autoregressive combinatorial optimizaiton solvers, covering our ICLR 2023 paper and SCIENTIA SINICA Informationis paper

  Language:Python33466

• alga-hopf/drl-graph-partitioning

  DRL models for graph partitioning and sparse matrix ordering.

  Language:Python29139

• wangjksjtu/rl-perturbed-reward

  Reinforcement Learning with Perturbed Reward, AAAI 2020

  Language:Python29413

• WindyLee0822/Process_Q_Model

  official implementation of paper "Process Reward Model with Q-value Rankings"

  Language:Python24121

• gaocrr/ELG

  Official implementation of IJCAI'24 paper "Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy"

  Language:Python21116

• Graph-COM/CO_ProxyDesign

  The repository for 'Unsupervised Learning for Combinatorial Optimization with Principled Proxy Design'

  Language:Python15010

• kaist-silab/meta-sage

  [ICML 2023] Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization

  Language:Python10100

• Graph-COM/Meta_CO

  the official repository of the paper unsupervised learning for combinatorial optimization needs meta learning

  Language:Python800