Timo Deist

According to our database, Timo Deist authored at least 10 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Multi-objective Learning Using HV Maximization.

Timo M. Deist, Monika Grewal, Frank J. W. M. Dankers, Tanja Alderliesten, Peter A. N. Bosman

Proceedings of the Evolutionary Multi-Criterion Optimization, 2023

2022

Hybridizing Hypervolume-Based Evolutionary Algorithms and Gradient Descent by Dynamic Resource Allocation.

Damy M. F. Ha, Timo M. Deist, Peter A. N. Bosman

Proceedings of the Parallel Problem Solving from Nature - PPSN XVII, 2022

2021

Multi-Objective Learning to Predict Pareto Fronts Using Hypervolume Maximization.

Timo M. Deist, Monika Grewal, Frank J. W. M. Dankers, Tanja Alderliesten, Peter A. N. Bosman

CoRR, 2021

2020

Identifying Properties of Real-World Optimisation Problems through a Questionnaire.

Koen van der Blom, Timo M. Deist, Vanessa Volz, Mariapia Marchi, Yusuke Nojima, Boris Naujoks, Akira Oyama, Tea Tusar

CoRR, 2020

Multi-objective Optimization by Uncrowded Hypervolume Gradient Ascent.

Timo M. Deist, Stefanus C. Maree, Tanja Alderliesten, Peter A. N. Bosman

Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

An end-to-end deep learning approach for landmark detection and matching in medical images.

Monika Grewal, Timo M. Deist, Jan Wiersma, Peter A. N. Bosman, Tanja Alderliesten

Proceedings of the Medical Imaging 2020: Image Processing, 2020

Towards realistic optimization benchmarks: a questionnaire on the properties of real-world problems.

Koen van der Blom, Timo M. Deist, Tea Tusar, Mariapia Marchi, Yusuke Nojima, Akira Oyama, Vanessa Volz, Boris Naujoks

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

2019

A Drug Recommendation System (Dr.S) for cancer cell lines.

Marleen Balvert, Georgios Patoulidis, Andrew Patti, Timo M. Deist, Christine Eyler, Bas E. Dutilh, Alexander Schönhuth, David Craft

CoRR, 2019

Simulation-assisted machine learning.

Timo Deist, Andrew Patti, Zhaoqi Wang, David Krane, Taylor Sorenson, David Craft

Bioinform., 2019

2015

Distributed Learning to Protect Privacy in Multi-centric Clinical Studies.

Andrea Damiani, Mauro Vallati, Roberto Gatta, Nicola Dinapoli, Arthur Jochems, Timo Deist, Johan van Soest, Andre Dekker, Vincenzo Valentini

Proceedings of the Artificial Intelligence in Medicine, 2015