Quan Minh Phan

Orcid: 0009-0002-8645-219X

According to our database1, Quan Minh Phan authored at least 11 papers between 2021 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
From hand-crafted metrics to evolved training-free performance predictors for neural architecture search via symbolic regression.
Neurocomputing, 2026

2025
From Hand-Crafted Metrics to Evolved Training-Free Performance Predictors for Neural Architecture Search via Genetic Programming.
CoRR, May, 2025

2024
Lightweight multi-objective evolutionary neural architecture search with low-cost proxy metrics.
Inf. Sci., January, 2024

Parameter-less Pareto local search for multi-objective neural architecture search with the Interleaved Multi-start Scheme.
Swarm Evol. Comput., 2024

Evaluating the Adversarial Robustness of Evolutionary Neural Architecture Search.
Proceedings of the International Conference on Multimedia Analysis and Pattern Recognition, 2024

Efficient Multi-Fidelity Neural Architecture Search with Zero-Cost Proxy-Guided Local Search.
Proceedings of the Genetic and Evolutionary Computation Conference, 2024

2023
Enhancing multi-objective evolutionary neural architecture search with training-free Pareto local search.
Appl. Intell., April, 2023

Enhancing Training-Free Multi-Objective Pruning-Based Neural Architecture Search with Low-Cost Local Search.
Proceedings of the International Conference on Computing and Communication Technologies, 2023

Pareto Local Search is Competitive with Evolutionary Algorithms for Multi-Objective Neural Architecture Search.
Proceedings of the Genetic and Evolutionary Computation Conference, 2023

2022
TF-MOPNAS: Training-free Multi-objective Pruning-Based Neural Architecture Search.
Proceedings of the Computational Collective Intelligence - 14th International Conference, 2022

2021
Enhancing Multi-objective Evolutionary Neural Architecture Search with Surrogate Models and Potential Point-Guided Local Searches.
Proceedings of the Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices, 2021


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