Papers with Code 아카이브

Model Optimization

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크
1
평가표
0
보존된 평가 기록
0

To Optimize already existing models in Training/Inferencing tasks.

데이터셋

출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.

Equilibrium-Traffic-Networks

This repository contains three graph datasets for the UE traffic assignment problem on Sioux-Falls, Eastern-Massachusetts and Anaheim networks in both dgl and pyg formats. The datasets are generated and used to train and evaluate models for solving the User Equilibrium (UE) problem on three transportation networks: - ** Sioux-Falls** - ** Eastern-Massachusetts** - **Anaheim ** These networks are sourced from the well-known "transport networks for research" repository. These datasets are generated for the study "A hybrid deep-learning-metaheuristic framework for bi-level network design problems" by Bahman Madadi and Gonçalo H. de Almeida Correia, published in Expert Systems with Applications. ## Metadata Detailed information can be found in the Metadata.md file. ## Links - Figshare Dataset Repository: Link ## References - Article: A hybrid deep-learning-metaheuristic framework for bi-level network design problems - Link - GitHub Repository: HDLMF_GIN-GA - Link - Primary Figshare Data Repository: Link Note: This dataset is maintained at Figshare.

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.