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Found 17 papers, 7 papers with code
The Unreasonable Effectiveness Of Early Discarding After One Epoch In Neural Network Hyperparameter Optimization
To reach high performance with deep learning, hyperparameter optimization (HPO) is essential.
RRR-Net: Reusing, Reducing, and Recycling a Deep Backbone Network
It has become mainstream in computer vision and other machine learning domains to reuse backbone networks pre-trained on large datasets as preprocessors.
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification
We introduce Meta-Album, an image classification meta-dataset designed to facilitate few-shot learning, transfer learning, meta-learning, among other tasks.
PyExperimenter: Easily distribute experiments and track results
PyExperimenter is a tool to facilitate the setup, documentation, execution, and subsequent evaluation of results from an empirical study of algorithms and in particular is designed to reduce the involved manual effort significantly.
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
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 resources, restricting their success to domains where such resources are available.
Learning Curves for Decision Making in Supervised Machine Learning -- A Survey
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 certain resource, e. g. the number of training examples or the number of training iterations.
Naive Automated Machine Learning
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.
Fast and Informative Model Selection using Learning Curve Cross-Validation
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 large portion of the given data and testing on the remaining data.
Towards Green Automated Machine Learning: Status Quo and Future Directions
Therefore, we first elaborate on how to quantify the environmental footprint of an AutoML tool.
Automated Machine Learning, Bounded Rationality, and Rational Metareasoning
The notion of bounded rationality originated from the insight that perfectly rational behavior cannot be realized by agents with limited cognitive or computational resources.
Replacing the Ex-Def Baseline in AutoML by Naive AutoML
Automated Machine Learning (AutoML) is the problem of automatically finding the pipeline with the best generalization performance on some given dataset.
Towards Model Selection using Learning Curve Cross-Validation
We run a large scale experiment on the 67 datasets from the AutoML benchmark, and empirically show that LCCV in over 90\% of the cases leads to similar performance (at most 0. 5\% difference) as 10-fold CV, but provides additional insights on the behaviour of a given model.
Naive Automated Machine Learning -- A Late Baseline for AutoML
Automated Machine Learning (AutoML) is the problem of automatically finding the pipeline with the best generalization performance on some given dataset.
Single and Parallel Machine Scheduling with Variable Release Dates
In this paper we study a simple extension of the total weighted flowtime minimization problem for single and identical parallel machines.
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis
In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.
Automated Multi-Label Classification based on ML-Plan
Automated machine learning (AutoML) has received increasing attention in the recent past.
ML-Plan: Automated machine learning via hierarchical planning
Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset).
