no code implementations • 16 May 2024 • Arwin Gansekoele, Alexios Balatsoukas-Stimming, Tom Brusse, Mark Hoogendoorn, Sandjai Bhulai, Rob van der Mei
As telecommunication systems evolve to meet increasing demands, integrating deep neural networks (DNNs) has shown promise in enhancing performance.
no code implementations • 16 May 2024 • Arwin Gansekoele, Tycho Bot, Rob van der Mei, Sandjai Bhulai, Mark Hoogendoorn
Third, we show that our method scales well to a dataset of over 1000 videos.
1 code implementation • 11 Jan 2023 • Joris Pries, Guus Berkelmans, Sandjai Bhulai, Rob van der Mei
We prove that our method has many useful properties, and accurately predicts the correct FI values for several cases where the ground truth FI can be derived in an exact manner.
no code implementations • 9 Jan 2023 • Joris Pries, Etienne van de Bijl, Jan Klein, Sandjai Bhulai, Rob van der Mei
The goal of this paper is to examine all baseline methods that are independent of feature values and determine which model is the `best' and why.
1 code implementation • 24 Mar 2022 • Etienne van de Bijl, Jan Klein, Joris Pries, Sandjai Bhulai, Mark Hoogendoorn, Rob van der Mei
Summarizing, the DD baseline is: (1) general, as it is applicable to all binary classification problems; (2) simple, as it is quickly determined without training or parameter-tuning; (3) informative, as insightful conclusions can be drawn from the results.
1 code implementation • 23 Mar 2022 • Guus Berkelmans, Joris Pries, Sandjai Bhulai, Rob van der Mei
To this end, we also provide Python code to determine the dependency function for use in practice.
no code implementations • 13 Aug 2021 • Jan Klein, Sandjai Bhulai, Mark Hoogendoorn, Rob van der Mei
These approaches choose speci? fic unlabeled instances by a query function that are expected to improve overall classi? cation performance.
no code implementations • 15 Mar 2021 • Daniel Hopman, Ger Koole, Rob van der Mei
After all, it is one of the inputs to an optimisation method which aim is to maximize revenue.