Test-Time Augmentation for Traveling Salesperson Problem
Fuente:
arXiv
Saved in:
| Main Authors: | Ishiyama, Ryo, Shirakawa, Takahiro, Uchida, Seiichi, Matsuo, Shinnosuke |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Bounding the Worst-class Error: A Boosting Approach
by: Saito, Yuya, et al.
Published: (2023)
by: Saito, Yuya, et al.
Published: (2023)
Computer-Aided Multi-Stroke Character Simplification by Stroke Removal
by: Ishiyama, Ryo, et al.
Published: (2025)
by: Ishiyama, Ryo, et al.
Published: (2025)
A First Guess is Rarely the Final Answer: Learning to Search in the Traveling Salesperson Problem
by: Garmendia, Andoni Irazusta
Published: (2026)
by: Garmendia, Andoni Irazusta
Published: (2026)
Tunable MAGMAX: Preference-Aware Model Merging for Continual Learning
by: Hiroshima, Kei, et al.
Published: (2026)
by: Hiroshima, Kei, et al.
Published: (2026)
TSPDiffuser: Diffusion Models as Learned Samplers for Traveling Salesperson Path Planning Problems
by: Yonetani, Ryo
Published: (2024)
by: Yonetani, Ryo
Published: (2024)
Instance-wise Supervision-level Optimization in Active Learning
by: Matsuo, Shinnosuke, et al.
Published: (2025)
by: Matsuo, Shinnosuke, et al.
Published: (2025)
Test-Time Augmentation Meets Variational Bayes
by: Kimura, Masanari, et al.
Published: (2024)
by: Kimura, Masanari, et al.
Published: (2024)
Unsupervised Learning for Solving the Travelling Salesman Problem
by: Min, Yimeng, et al.
Published: (2023)
by: Min, Yimeng, et al.
Published: (2023)
C-voting: Confidence-Based Test-Time Voting without Explicit Energy Functions
by: Kubo, Kenji, et al.
Published: (2026)
by: Kubo, Kenji, et al.
Published: (2026)
Hybrid Quantum-Classical Optimisation of Traveling Salesperson Problem
by: Lytrosyngounis, Christos, et al.
Published: (2025)
by: Lytrosyngounis, Christos, et al.
Published: (2025)
Local Mixtures of Experts: Essentially Free Test-Time Training via Model Merging
by: Bertolissi, Ryo, et al.
Published: (2025)
by: Bertolissi, Ryo, et al.
Published: (2025)
Spatial-Aware Deep Reinforcement Learning for the Traveling Officer Problem
by: Strauß, Niklas, et al.
Published: (2024)
by: Strauß, Niklas, et al.
Published: (2024)
Distilling Privileged Information for Dubins Traveling Salesman Problems with Neighborhoods
by: Shin, Min Kyu, et al.
Published: (2024)
by: Shin, Min Kyu, et al.
Published: (2024)
On Size and Hardness Generalization in Unsupervised Learning for the Travelling Salesman Problem
by: Min, Yimeng, et al.
Published: (2024)
by: Min, Yimeng, et al.
Published: (2024)
Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation
by: Gong, Peiliang, et al.
Published: (2025)
by: Gong, Peiliang, et al.
Published: (2025)
Diversity Optimization for Travelling Salesman Problem via Deep Reinforcement Learning
by: Li, Qi, et al.
Published: (2025)
by: Li, Qi, et al.
Published: (2025)
Generative Modeling for Robust Deep Reinforcement Learning on the Traveling Salesman Problem
by: Li, Michael, et al.
Published: (2025)
by: Li, Michael, et al.
Published: (2025)
Cross-Sample Augmented Test-Time Adaptation for Personalized Intraoperative Hypotension Prediction
by: Li, Kanxue, et al.
Published: (2025)
by: Li, Kanxue, et al.
Published: (2025)
Deep Reinforcement Learning for Traveling Purchaser Problems
by: Yuan, Haofeng, et al.
Published: (2024)
by: Yuan, Haofeng, et al.
Published: (2024)
Adaptation and Fine-tuning with TabPFN for Travelling Salesman Problem
by: Vu, Nguyen Gia Hien, et al.
Published: (2025)
by: Vu, Nguyen Gia Hien, et al.
Published: (2025)
Looking Ahead to Avoid Being Late: Solving Hard-Constrained Traveling Salesman Problem
by: Chen, Jingxiao, et al.
Published: (2024)
by: Chen, Jingxiao, et al.
Published: (2024)
A Unified Deep Reinforcement Learning Approach for Close Enough Traveling Salesman Problem
by: Fan, Mingfeng, et al.
Published: (2025)
by: Fan, Mingfeng, et al.
Published: (2025)
An End-to-End Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drones
by: Zeng, Taihelong, et al.
Published: (2025)
by: Zeng, Taihelong, et al.
Published: (2025)
NoiseCollage: A Layout-Aware Text-to-Image Diffusion Model Based on Noise Cropping and Merging
by: Shirakawa, Takahiro, et al.
Published: (2024)
by: Shirakawa, Takahiro, et al.
Published: (2024)
Surrogate Benchmarks for Model Merging Optimization
by: Akizuki, Rio, et al.
Published: (2025)
by: Akizuki, Rio, et al.
Published: (2025)
Self-Harmony: Learning to Harmonize Self-Supervision and Self-Play in Test-Time Reinforcement Learning
by: Wang, Ru, et al.
Published: (2025)
by: Wang, Ru, et al.
Published: (2025)
Construct, Merge, Solve & Adapt with Reinforcement Learning for the min-max Multiple Traveling Salesman Problem
by: Rodríguez-Corominas, Guillem, et al.
Published: (2026)
by: Rodríguez-Corominas, Guillem, et al.
Published: (2026)
Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman Problem
by: Goh, Yong Liang, et al.
Published: (2022)
by: Goh, Yong Liang, et al.
Published: (2022)
Position: Rethinking Post-Hoc Search-Based Neural Approaches for Solving Large-Scale Traveling Salesman Problems
by: Xia, Yifan, et al.
Published: (2024)
by: Xia, Yifan, et al.
Published: (2024)
iMTSP: Solving Min-Max Multiple Traveling Salesman Problem with Imperative Learning
by: Guo, Yifan, et al.
Published: (2024)
by: Guo, Yifan, et al.
Published: (2024)
MoireDB: Formula-generated Interference-fringe Image Dataset
by: Matsuo, Yuto, et al.
Published: (2025)
by: Matsuo, Yuto, et al.
Published: (2025)
Log-Augmented Generation: Scaling Test-Time Reasoning with Reusable Computation
by: Chen, Peter Baile, et al.
Published: (2025)
by: Chen, Peter Baile, et al.
Published: (2025)
Equivariant Eikonal Neural Networks: Grid-Free, Scalable Travel-Time Prediction on Homogeneous Spaces
by: García-Castellanos, Alejandro, et al.
Published: (2025)
by: García-Castellanos, Alejandro, et al.
Published: (2025)
Bandit-Based Prompt Design Strategy Selection Improves Prompt Optimizers
by: Ashizawa, Rin, et al.
Published: (2025)
by: Ashizawa, Rin, et al.
Published: (2025)
Principled Data Augmentation for Learning to Solve Quadratic Programming Problems
by: Qian, Chendi, et al.
Published: (2025)
by: Qian, Chendi, et al.
Published: (2025)
Prompting Test-Time Scaling Is A Strong LLM Reasoning Data Augmentation
by: Bsharat, Sondos Mahmoud, et al.
Published: (2025)
by: Bsharat, Sondos Mahmoud, et al.
Published: (2025)
Retrieval Augmented Time Series Forecasting
by: Tire, Kutay, et al.
Published: (2024)
by: Tire, Kutay, et al.
Published: (2024)
Test Time Learning for Time Series Forecasting
by: Christou, Panayiotis, et al.
Published: (2024)
by: Christou, Panayiotis, et al.
Published: (2024)
Learning to Discover at Test Time
by: Yuksekgonul, Mert, et al.
Published: (2026)
by: Yuksekgonul, Mert, et al.
Published: (2026)
HardCore Generation: Generating Hard UNSAT Problems for Data Augmentation
by: Cotnareanu, Joseph, et al.
Published: (2024)
by: Cotnareanu, Joseph, et al.
Published: (2024)
Similar Items
-
Bounding the Worst-class Error: A Boosting Approach
by: Saito, Yuya, et al.
Published: (2023) -
Computer-Aided Multi-Stroke Character Simplification by Stroke Removal
by: Ishiyama, Ryo, et al.
Published: (2025) -
A First Guess is Rarely the Final Answer: Learning to Search in the Traveling Salesperson Problem
by: Garmendia, Andoni Irazusta
Published: (2026) -
Tunable MAGMAX: Preference-Aware Model Merging for Continual Learning
by: Hiroshima, Kei, et al.
Published: (2026) -
TSPDiffuser: Diffusion Models as Learned Samplers for Traveling Salesperson Path Planning Problems
by: Yonetani, Ryo
Published: (2024)