Moritz Vinzent Seiler

Orcid: 0000-0002-1750-9060

Affiliations:
  • University of Münster, Statistics and Optimization, Germany


According to our database1, Moritz Vinzent Seiler authored at least 13 papers between 2020 and 2024.

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

Timeline

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Bibliography

2024
Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems.
CoRR, 2024

2023
A study on the effects of normalized TSP features for automated algorithm selection.
Theor. Comput. Sci., 2023

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research.
CoRR, 2023

Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2023

2022
Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods.
Proceedings of the Parallel Problem Solving from Nature - PPSN XVII, 2022

Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches.
Proceedings of the Workshop Proceedings of the 16th International AAAI Conference on Web and Social Media, 2022

A collection of deep learning-based feature-free approaches for characterizing single-objective continuous fitness landscapes.
Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, USA, July 9, 2022

2021
Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

<tt>RP-Mod</tt>&<tt>RP-Crowd: </tt> Moderator- and Crowd-Annotated German News Comment Datasets.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

On the potential of normalized TSP features for automated algorithm selection.
Proceedings of the FOGA '21: Foundations of Genetic Algorithms XVI, 2021

2020
Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.
Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

FakeYou! - A Gamified Approach for Building and Evaluating Resilience Against Fake News.
Proceedings of the Disinformation in Open Online Media, 2020

Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020


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