Complexity Scaling Laws for Neural Models using Combinatorial Optimization
Fuente:
arXiv
Saved in:
| Main Authors: | Weissman, Lowell, Krumdick, Michael, Abbott, A. Lynn |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Probing Neural Combinatorial Optimization Models
by: Zhang, Zhiqin, et al.
Published: (2025)
by: Zhang, Zhiqin, et al.
Published: (2025)
On the Optimizer Dependence of Neural Scaling Laws
by: Ramani, Vansh, et al.
Published: (2026)
by: Ramani, Vansh, et al.
Published: (2026)
Neural Neural Scaling Laws
by: Hu, Michael Y., et al.
Published: (2026)
by: Hu, Michael Y., et al.
Published: (2026)
Scaling Combinatorial Optimization Neural Improvement Heuristics with Online Search and Adaptation
by: Verdù, Federico Julian Camerota, et al.
Published: (2024)
by: Verdù, Federico Julian Camerota, et al.
Published: (2024)
Data-Error Scaling Laws in Machine Learning on Combinatorial Mutation-prone Sets: Proteins and Small Molecules
by: Doffini, Vanni, et al.
Published: (2024)
by: Doffini, Vanni, et al.
Published: (2024)
Recurrent State Encoders for Efficient Neural Combinatorial Optimization
by: Dernedde, Tim, et al.
Published: (2025)
by: Dernedde, Tim, et al.
Published: (2025)
Multi-Action Self-Improvement for Neural Combinatorial Optimization
by: Luttmann, Laurin, et al.
Published: (2025)
by: Luttmann, Laurin, et al.
Published: (2025)
Leader Reward for POMO-Based Neural Combinatorial Optimization
by: Wang, Chaoyang, et al.
Published: (2024)
by: Wang, Chaoyang, et al.
Published: (2024)
Rethinking Efficiency in Neural Combinatorial Optimization: Batched Preference Optimization with Mamba
by: Xu, Zhenxing, et al.
Published: (2026)
by: Xu, Zhenxing, et al.
Published: (2026)
Scaling Laws for Neural Material Models
by: Trikha, Akshay, et al.
Published: (2025)
by: Trikha, Akshay, et al.
Published: (2025)
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization
by: Luo, Fu, et al.
Published: (2023)
by: Luo, Fu, et al.
Published: (2023)
Geometric Algorithms for Neural Combinatorial Optimization with Constraints
by: Karalias, Nikolaos, et al.
Published: (2025)
by: Karalias, Nikolaos, et al.
Published: (2025)
On the Invariance and Generality of Neural Scaling Laws
by: Han, Xing, et al.
Published: (2026)
by: Han, Xing, et al.
Published: (2026)
Combinatorial Optimization with Automated Graph Neural Networks
by: Liu, Yang, et al.
Published: (2024)
by: Liu, Yang, et al.
Published: (2024)
Efficient Training of Multi-task Neural Solver for Combinatorial Optimization
by: Wang, Chenguang, et al.
Published: (2023)
by: Wang, Chenguang, et al.
Published: (2023)
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning
by: Meng, Xiang, et al.
Published: (2024)
by: Meng, Xiang, et al.
Published: (2024)
Towards Robust Scaling Laws for Optimizers
by: Volkova, Alexandra, et al.
Published: (2026)
by: Volkova, Alexandra, et al.
Published: (2026)
Neural Solver Selection for Combinatorial Optimization
by: Gao, Chengrui, et al.
Published: (2024)
by: Gao, Chengrui, et al.
Published: (2024)
On Neural Scaling Laws for Weather Emulation through Continual Training
by: Subramanian, Shashank, et al.
Published: (2026)
by: Subramanian, Shashank, et al.
Published: (2026)
Divide and Learn: Multi-Objective Combinatorial Optimization at Scale
by: Singh, Esha, et al.
Published: (2026)
by: Singh, Esha, et al.
Published: (2026)
Neural Circuit Diagrams: Robust Diagrams for the Communication, Implementation, and Analysis of Deep Learning Architectures
by: Abbott, Vincent
Published: (2024)
by: Abbott, Vincent
Published: (2024)
A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization
by: Sanokowski, Sebastian, et al.
Published: (2024)
by: Sanokowski, Sebastian, et al.
Published: (2024)
Unified Neural Network Scaling Laws and Scale-time Equivalence
by: Boopathy, Akhilan, et al.
Published: (2024)
by: Boopathy, Akhilan, et al.
Published: (2024)
BOPO: Neural Combinatorial Optimization via Best-anchored and Objective-guided Preference Optimization
by: Liao, Zijun, et al.
Published: (2025)
by: Liao, Zijun, et al.
Published: (2025)
Configuration-to-Performance Scaling Law with Neural Ansatz
by: Zhang, Huaqing, et al.
Published: (2026)
by: Zhang, Huaqing, et al.
Published: (2026)
Breaking Neural Network Scaling Laws with Modularity
by: Boopathy, Akhilan, et al.
Published: (2024)
by: Boopathy, Akhilan, et al.
Published: (2024)
Decision-focused Graph Neural Networks for Combinatorial Optimization
by: Liu, Yang, et al.
Published: (2024)
by: Liu, Yang, et al.
Published: (2024)
Self-Improved Learning for Scalable Neural Combinatorial Optimization
by: Luo, Fu, et al.
Published: (2024)
by: Luo, Fu, et al.
Published: (2024)
Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement
by: Pirnay, Jonathan, et al.
Published: (2024)
by: Pirnay, Jonathan, et al.
Published: (2024)
Weisfeiler Lehman Test on Combinatorial Complexes: Generalized Expressive Power of Topological Neural Networks
by: Chen, Jiawen, et al.
Published: (2026)
by: Chen, Jiawen, et al.
Published: (2026)
A Dynamical Model of Neural Scaling Laws
by: Bordelon, Blake, et al.
Published: (2024)
by: Bordelon, Blake, et al.
Published: (2024)
TopoTune : A Framework for Generalized Combinatorial Complex Neural Networks
by: Papillon, Mathilde, et al.
Published: (2024)
by: Papillon, Mathilde, et al.
Published: (2024)
Preference Optimization for Combinatorial Optimization Problems
by: Pan, Mingjun, et al.
Published: (2025)
by: Pan, Mingjun, et al.
Published: (2025)
Towards Neural Scaling Laws on Graphs
by: Liu, Jingzhe, et al.
Published: (2024)
by: Liu, Jingzhe, et al.
Published: (2024)
Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling
by: Li, Chaojian, et al.
Published: (2025)
by: Li, Chaojian, et al.
Published: (2025)
Towards Neural Scaling Laws for Time Series Foundation Models
by: Yao, Qingren, et al.
Published: (2024)
by: Yao, Qingren, et al.
Published: (2024)
Neural Tractability via Structure: Learning-Augmented Algorithms for Graph Combinatorial Optimization
by: Li, Jialiang, et al.
Published: (2025)
by: Li, Jialiang, et al.
Published: (2025)
Enabling Population-Based Architectures for Neural Combinatorial Optimization
by: Garmendia, Andoni Irazusta, et al.
Published: (2026)
by: Garmendia, Andoni Irazusta, et al.
Published: (2026)
ASAP: Exploiting the Satisficing Generalization Edge in Neural Combinatorial Optimization
by: Fang, Han, et al.
Published: (2025)
by: Fang, Han, et al.
Published: (2025)
RRNCO: Towards Real-World Routing with Neural Combinatorial Optimization
by: Son, Jiwoo, et al.
Published: (2025)
by: Son, Jiwoo, et al.
Published: (2025)
Similar Items
-
Probing Neural Combinatorial Optimization Models
by: Zhang, Zhiqin, et al.
Published: (2025) -
On the Optimizer Dependence of Neural Scaling Laws
by: Ramani, Vansh, et al.
Published: (2026) -
Neural Neural Scaling Laws
by: Hu, Michael Y., et al.
Published: (2026) -
Scaling Combinatorial Optimization Neural Improvement Heuristics with Online Search and Adaptation
by: Verdù, Federico Julian Camerota, et al.
Published: (2024) -
Data-Error Scaling Laws in Machine Learning on Combinatorial Mutation-prone Sets: Proteins and Small Molecules
by: Doffini, Vanni, et al.
Published: (2024)