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2020 – today
- 2023
[c6]Timo M. Deist
, Monika Grewal, Frank J. W. M. Dankers, Tanja Alderliesten, Peter A. N. Bosman:
Multi-objective Learning Using HV Maximization. EMO 2023: 103-117- [p1]Koen van der Blom, Timo M. Deist, Vanessa Volz, Mariapia Marchi, Yusuke Nojima, Boris Naujoks, Akira Oyama, Tea Tusar:
Identifying Properties of Real-World Optimisation Problems Through a Questionnaire. Many-Criteria Optimization and Decision Analysis 2023: 59-80 - 2022
- [c5]Damy M. F. Ha, Timo M. Deist, Peter A. N. Bosman:
Hybridizing Hypervolume-Based Evolutionary Algorithms and Gradient Descent by Dynamic Resource Allocation. PPSN (2) 2022: 179-192 - 2021
- [i6]Timo M. Deist, Monika Grewal, Frank J. W. M. Dankers, Tanja Alderliesten, Peter A. N. Bosman:
Multi-Objective Learning to Predict Pareto Fronts Using Hypervolume Maximization. CoRR abs/2102.04523 (2021) - 2020
- [c4]Koen van der Blom, Timo M. Deist, Tea Tusar, Mariapia Marchi, Yusuke Nojima, Akira Oyama, Vanessa Volz, Boris Naujoks:
Towards realistic optimization benchmarks: a questionnaire on the properties of real-world problems. GECCO Companion 2020: 293-294 - [c3]Monika Grewal, Timo M. Deist, Jan Wiersma, Peter A. N. Bosman, Tanja Alderliesten:
An end-to-end deep learning approach for landmark detection and matching in medical images. Medical Imaging: Image Processing 2020: 1131328 - [c2]Timo M. Deist, Stefanus C. Maree, Tanja Alderliesten, Peter A. N. Bosman:
Multi-objective Optimization by Uncrowded Hypervolume Gradient Ascent. PPSN (2) 2020: 186-200 - [i5]Monika Grewal, Timo M. Deist, Jan Wiersma, Peter A. N. Bosman, Tanja Alderliesten:
An End-to-end Deep Learning Approach for Landmark Detection and Matching in Medical Images. CoRR abs/2001.07434 (2020) - [i4]Koen van der Blom, Timo M. Deist, Tea Tusar, Mariapia Marchi, Yusuke Nojima, Akira Oyama, Vanessa Volz, Boris Naujoks:
Towards Realistic Optimization Benchmarks: A Questionnaire on the Properties of Real-World Problems. CoRR abs/2004.06395 (2020) - [i3]Koen van der Blom, Timo M. Deist, Vanessa Volz, Mariapia Marchi, Yusuke Nojima, Boris Naujoks, Akira Oyama, Tea Tusar:
Identifying Properties of Real-World Optimisation Problems through a Questionnaire. CoRR abs/2011.05547 (2020)
2010 – 2019
- 2019
- [j1]Timo Deist, Andrew Patti, Zhaoqi Wang, David Krane, Taylor Sorenson, David Craft:
Simulation-assisted machine learning. Bioinform. 35(20): 4072-4080 (2019) - [i2]Marleen Balvert, Georgios Patoulidis, Andrew Patti, Timo M. Deist, Christine Eyler, Bas E. Dutilh, Alexander Schönhuth, David Craft:
A Drug Recommendation System (Dr.S) for cancer cell lines. CoRR abs/1912.11548 (2019) - 2018
- [i1]Timo Deist, Andrew Patti, Zhaoqi Wang, David Krane, Taylor Sorenson, David Craft:
Simulation assisted machine learning. CoRR abs/1802.05688 (2018) - 2015
- [c1]Andrea Damiani, Mauro Vallati, Roberto Gatta, Nicola Dinapoli, Arthur Jochems, Timo Deist, Johan van Soest, Andre Dekker, Vincenzo Valentini:
Distributed Learning to Protect Privacy in Multi-centric Clinical Studies. AIME 2015: 65-75
Coauthor Index
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