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Daniela Thyssens
Daniela Thyssens
Research Assistant at Information Systems and Machine Learning Lab (ISMLL)
Verified email at ismll.de
Title
Cited by
Cited by
Year
Do we really need deep learning models for time series forecasting?
S Elsayed, D Thyssens, A Rashed, HS Jomaa, L Schmidt-Thieme
arXiv preprint arXiv:2101.02118, 2021
1212021
Do we really need deep learning models for time series forecasting? arXiv 2021
S Elsayed, D Thyssens, A Rashed, HS Jomaa, L Schmidt-Thieme
arXiv preprint arXiv:2101.02118, 0
13
Learning to control local search for combinatorial optimization
JK Falkner, D Thyssens, A Bdeir, L Schmidt-Thieme
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022
102022
Large neighborhood search based on neural construction heuristics
JK Falkner, D Thyssens, L Schmidt-Thieme
arXiv preprint arXiv:2205.00772, 2022
62022
Do we really need deep learning models for time series forecasting?, 2021
S Elsayed, D Thyssens, A Rashed, HS Jomaa, L Schmidt-Thieme
URL https://arxiv. org/abs/2101 2118, 2021
52021
Supervised permutation invariant networks for solving the CVRP with bounded fleet size
D Thyssens, J Falkner, L Schmidt-Thieme
arXiv preprint arXiv:2201.01529, 2022
32022
Exploring the influence of data aggregation in parking prediction
S Elsayed, D Thyssens, S Chamurally, A Tariq, HS Jomaa
Database and Expert Systems Applications: DEXA 2020 International Workshops …, 2020
12020
Moco: A Learnable Meta Optimizer for Combinatorial Optimization
T Dernedde, D Thyssens, S Dittrich, M Stubbemann, L Schmidt-Thieme
arXiv preprint arXiv:2402.04915, 2024
2024
Routing Arena: A Benchmark Suite for Neural Routing Solvers
D Thyssens, T Dernedde, JK Falkner, L Schmidt-Thieme
arXiv preprint arXiv:2310.04140, 2023
2023
DeepStay: Stay Region Extraction from Location Trajectories using Weak Supervision
C Löwens, D Thyssens, E Andersson, C Jenkins, L Schmidt-Thieme
2023 IEEE 26th International Conference on Intelligent Transportation …, 2023
2023
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