Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Utrecht UniversityNL210 papers
- Delft University of TechnologyNL196 papers
- Centre National de la Recherche ScientifiqueFR195 papers
- Centrum Wiskunde & InformaticaNL163 papers
- University of TwenteNL158 papers
- University of AmsterdamNL113 papers
- Radboud University NijmegenNL89 papers
- University of Rome Tor VergataIT84 papers
- Vrije Universiteit AmsterdamNL79 papers
- Leiden UniversityNL72 papers
- University of GroningenNL58 papers
- Philips (Netherlands)NL56 papers
13 papers · 2 filters
OpenML-Python: an extensible Python API for OpenML
Matthias Feurer, Jan N. van Rijn, Arlind Kadra +6
OpenML is an online platform for open science collaboration in machine learning, used to share datasets and results of machine learning experiments. In this paper we introduce Open…
Deep Structured Mixtures of Gaussian Processes
Martin Trapp, Robert Peharz, Franz Pernkopf +1
Gaussian Processes (GPs) are powerful non-parametric Bayesian regression models that allow exact posterior inference, but exhibit high computational and memory costs. In order to i…
Static force field representation of environments based on agents nonlinear motions
Damian Campo, Alejandro Betancourt, Lucio Marcenaro +1
This paper presents a methodology that aims at the incremental representation of areas inside environments in terms of attractive forces. It is proposed a parametric representation…
B-Spline CNNs on Lie Groups
Erik J Bekkers
Group convolutional neural networks (G-CNNs) can be used to improve classical CNNs by equipping them with the geometric structure of groups. Central in the success of G-CNNs is the…
Task Classification Model for Visual Fixation, Exploration, and Search
Ayush Kumar, Anjul Tyagi, Michael Burch +2
Yarbus' claim to decode the observer's task from eye movements has received mixed reactions. In this paper, we have supported the hypothesis that it is possible to decode the task.…
Multi-task Self-Supervised Learning for Human Activity Detection
Aaqib Saeed, Tanir Ozcelebi, Johan Lukkien
Deep learning methods are successfully used in applications pertaining to ubiquitous computing, health, and well-being. Specifically, the area of human activity recognition (HAR) i…
Case-Based Reasoning for Assisting Domain Experts in Processing Fraud Alerts of Black-Box Machine Learning Models
Hilde J. P. Weerts, Werner van Ipenburg, Mykola Pechenizkiy
In many contexts, it can be useful for domain experts to understand to what extent predictions made by a machine learning model can be trusted. In particular, estimates of trustwor…
A Human-Grounded Evaluation of SHAP for Alert Processing
Hilde J. P. Weerts, Werner van Ipenburg, Mykola Pechenizkiy
In the past years, many new explanation methods have been proposed to achieve interpretability of machine learning predictions. However, the utility of these methods in practical a…
An Open Source AutoML Benchmark
Pieter Gijsbers, Erin LeDell, Janek Thomas +3
In recent years, an active field of research has developed around automated machine learning (AutoML). Unfortunately, comparing different AutoML systems is hard and often done inco…
A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers
Rafael Gomes Mantovani, André Luis Debiaso Rossi, Edesio Alcobaça +2
For many machine learning algorithms, predictive performance is critically affected by the hyperparameter values used to train them. However, tuning these hyperparameters can come…
Hierarchical Annotation of Images with Two-Alternative-Forced-Choice Metric Learning
Niels Hellinga, Vlado Menkovski
Many tasks such as retrieval and recommendations can significantly benefit from structuring the data, commonly in a hierarchical way. To achieve this through annotations of high di…
Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data
Xin Du, Lei Sun, Wouter Duivesteijn +2
Learning causal effects from observational data greatly benefits a variety of domains such as health care, education and sociology. For instance, one could estimate the impact of a…
Diffusion Variational Autoencoders
Luis A. Pérez Rey, Vlado Menkovski, Jacobus W. Portegies
A standard Variational Autoencoder, with a Euclidean latent space, is structurally incapable of capturing topological properties of certain datasets. To remove topological obstruct…