https://github.com/ruizhong961230/llmo
공식 코드Large Language Model Assisted Adversarial Robustness Neural Architecture Search
Papers with Code 아카이브
Combinatorial Optimization
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Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ryan-thompson/ProDAG.jl
공식 코드ProDAG: Projected Variational Inference for Directed Acyclic Graphs
https://github.com/rymoah/BalancedCrossoverGA
공식 코드https://github.com/salehinejad/srocket
공식 코드S-Rocket: Selective Random Convolution Kernels for Time Series Classification
https://github.com/sayal-k/dso4ns
공식 코드https://github.com/shengyu-feng/rld4co
공식 코드Regularized Langevin Dynamics for Combinatorial Optimization
https://github.com/sherrylixuecheng/EMQAOA-DARBO
공식 코드Quantum approximate optimization via learning-based adaptive optimization
https://github.com/skadio/balans
공식 코드https://github.com/sleepymalc/Travel-the-Same-Path
공식 코드https://github.com/snu-mllab/greedypolicyformoco
공식 코드https://github.com/snu-mllab/parsimonious-blackbox-attack
공식 코드Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization
https://github.com/starjob42/Starjob
공식 코드https://github.com/stevezhuo/fis-one
공식 코드FIS-ONE: Floor Identification System with One Label for Crowdsourced RF Signals
https://github.com/stiglidu/autotruss
공식 코드https://github.com/sunnweiwei/co-bench
공식 코드CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial Optimization
https://github.com/supreethmv/bilp-q
공식 코드https://github.com/sydney-machine-learning/MEHH_RCPSP
공식 코드MAP-Elites based Hyper-Heuristic for the Resource Constrained Project Scheduling Problem
https://github.com/taozeyi1990/laws
공식 코드LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks
https://github.com/tharaka-perera/flex-net
공식 코드Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex Networks
https://github.com/thinklab-sjtu/nar-co-solver
공식 코드https://github.com/thinklab-sjtu/ppo-bihyb
공식 코드A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs
https://github.com/thinklab-sjtu/predictiveco-benchmark
공식 코드Benchmarking PtO and PnO Methods in the Predictive Combinatorial Optimization Regime
https://github.com/thinklab-sjtu/rgm
공식 코드Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching
https://github.com/thunlp/SememePSO-Attack
공식 코드Word-level Textual Adversarial Attacking as Combinatorial Optimization
https://github.com/timd3/moco
공식 코드Moco: A Learnable Meta Optimizer for Combinatorial Optimization
https://github.com/toenshoff/runcsp-pytorch
공식 코드https://github.com/tomdbar/eco-dqn
공식 코드Exploratory Combinatorial Optimization with Reinforcement Learning
https://github.com/tumBAIS/ML-CO-pipeline-AMoD-control
공식 코드Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control
https://github.com/tumbais/euro-meets-neurips-2022
공식 코드https://github.com/tumbais/ml-co-pipeline-trafficprediction
공식 코드WardropNet: Traffic Flow Predictions via Equilibrium-Augmented Learning
https://github.com/tumbais/risksensitivesacforrobustdrlunderdistshifts
공식 코드Risk-Sensitive Soft Actor-Critic for Robust Deep Reinforcement Learning under Distribution Shifts
https://github.com/tumbais/structured-rl
공식 코드Structured Reinforcement Learning for Combinatorial Decision-Making
https://github.com/txhaug/nisq-sdp
공식 코드Noisy intermediate-scale quantum algorithm for semidefinite programming
https://github.com/udeshmg/gta-rl
공식 코드Solving Dynamic Graph Problems with Multi-Attention Deep Reinforcement Learning
https://github.com/uic-jq/iopo
공식 코드A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing System
https://github.com/vdvf96/Differentiable_Model_Selection_for_Ensemble_Learning
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.