50 citations · 124 across the 14 of their papers we have counts for
9 papers · 1 filter
RRR-Net: Reusing, Reducing, and Recycling a Deep Backbone Network
Haozhe Sun, Isabelle Guyon, Felix Mohr +1
It has become mainstream in computer vision and other machine learning domains to reuse backbone networks pre-trained on large datasets as preprocessors. Typically, the last layer…
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
Adrian El Baz, Ihsan Ullah, Edesio Alcobaça +17
Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resourc…
Learning Curves for Decision Making in Supervised Machine Learning: A Survey
Felix Mohr, Jan N. van Rijn
Learning curves are a concept from social sciences that has been adopted in the context of machine learning to assess the performance of a learning algorithm with respect to a cert…
Naive Automated Machine Learning
Felix Mohr, Marcel Wever
An essential task of Automated Machine Learning (AutoML) is the problem of automatically finding the pipeline with the best generalization performance on a given dataset. This prob…
Fast and Informative Model Selection using Learning Curve Cross-Validation
Felix Mohr, Jan N. van Rijn
Common cross-validation (CV) methods like k-fold cross-validation or Monte-Carlo cross-validation estimate the predictive performance of a learner by repeatedly training it on a la…
Towards Green Automated Machine Learning: Status Quo and Future Directions
Tanja Tornede, Alexander Tornede, Jonas Hanselle +3
Automated machine learning (AutoML) strives for the automatic configuration of machine learning algorithms and their composition into an overall (software) solution - a machine lea…
Naive Automated Machine Learning -- A Late Baseline for AutoML
Felix Mohr, Marcel Wever
Automated Machine Learning (AutoML) is the problem of automatically finding the pipeline with the best generalization performance on some given dataset. AutoML has received enormou…
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis
Alexander Tornede, Marcel Wever, Stefan Werner +2
Algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem…
Automated Multi-Label Classification based on ML-Plan
Marcel Wever, Felix Mohr, Eyke Hüllermeier
Automated machine learning (AutoML) has received increasing attention in the recent past. While the main tools for AutoML, such as Auto-WEKA, TPOT, and auto-sklearn, mainly deal wi…