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Found 32 papers, 15 papers with code
Warm-starting DARTS using meta-learning
Neural architecture search (NAS) has shown great promise in the field of automated machine learning (AutoML).
Advances in MetaDL: AAAI 2021 challenge and workshop
Winning methods featured various classifiers trained on top of the second last layer of popular CNN backbones, fined-tuned on the meta-training data (not necessarily in an episodic manner), then trained on the labeled support and tested on the unlabeled query sets of the meta-test data.
Online AutoML: An adaptive AutoML framework for online learning
For this purpose, we design an adaptive Online Automated Machine Learning (OAML) system, searching the complete pipeline configuration space of online learners, including preprocessing algorithms and ensembling techniques.
Automated Reinforcement Learning: An Overview
Automated RL provides a framework in which different components of RL including MDP modeling, algorithm selection and hyper-parameter optimization are modeled and defined automatically.
Frugal Machine Learning
Machine learning, already at the core of increasingly many systems and applications, is set to become even more ubiquitous with the rapid rise of wearable devices and the Internet of Things.
From Strings to Data Science: a Practical Framework for Automated String Handling
Many machine learning libraries require that string features be converted to a numerical representation for the models to work as intended.
Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification
There is currently no consensus on how to choose appropriate source data, and in the literature we can find both evidence of favoring large natural image datasets such as ImageNet, and evidence of favoring more specialized medical datasets.
Meta-Learning for Symbolic Hyperparameter Defaults
Hyperparameter optimization in machine learning (ML) deals with the problem of empirically learning an optimal algorithm configuration from data, usually formulated as a black-box optimization problem.
Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms
One drawback however is the high computational complexity and high memory consumption of CNNs which makes them unfeasible for execution on embedded platforms which are constrained on physical resources needed to support CNNs.
A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications
Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensive to evaluate.
Bayesian Optimisation
High Energy Physics - Phenomenology
Computational Physics
Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models
Bayesian Optimization is a popular tool for tuning algorithms in automatic machine learning (AutoML) systems.
Theory-based Habit Modeling for Enhancing Behavior Prediction
Psychological theories of habit posit that when a strong habit is formed through behavioral repetition, it can trigger behavior automatically in the same environment.
Aerial Imagery Pixel-level Segmentation
Hence, we also propose a new benchmark on the DroneDeploy test set using the best performing DeepLabv3+ Xception65 architecture, with a mIOU score of 52. 5%.
Ranked #1 on
Semantic Segmentation
on DroneDeploy
Importance of Tuning Hyperparameters of Machine Learning Algorithms
The performance of many machine learning algorithms depends on their hyperparameter settings.
GAMA: a General Automated Machine learning Assistant
The General Automated Machine learning Assistant (GAMA) is a modular AutoML system developed to empower users to track and control how AutoML algorithms search for optimal machine learning pipelines, and facilitate AutoML research itself.
Adaptation Strategies for Automated Machine Learning on Evolving Data
To that end, we propose 6 concept drift adaptation strategies and evaluate their effectiveness on different AutoML approaches.
OpenML-Python: an extensible Python API for OpenML
It also provides functionality to conduct machine learning experiments, upload the results to OpenML, and reproduce results which are stored on OpenML.
An Open Source AutoML Benchmark
In recent years, an active field of research has developed around automated machine learning (AutoML).
A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers
For many machine learning algorithms, predictive performance is critically affected by the hyperparameter values used to train them.
MLSys: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoption.
An empirical study on hyperparameter tuning of decision trees
Experimental results indicate that hyperparameter tuning provides statistically significant improvements for C4. 5 and CTree in only one-third of the datasets, and in most of the datasets for CART.
Transformative Machine Learning
In all three problems, transformative machine learning significantly outperforms the best intrinsic representation.
Meta-Learning: A Survey
In this chapter, we provide an overview of the state of the art in this fascinating and continuously evolving field.
Characterizing classification datasets: a study of meta-features for meta-learning
These characterizations, also called meta-features, describe properties of the data which are predictive for the performance of machine learning algorithms trained on them.
ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies
The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology that provides a set of classes, properties, and restrictions for representing and interchanging information on machine learning algorithms, datasets, and experiments.
Layered TPOT: Speeding up Tree-based Pipeline Optimization
With the demand for machine learning increasing, so does the demand for tools which make it easier to use.
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery
We first carried out the most comprehensive ever comparison of machine learning methods for QSAR learning: 18 regression methods, 6 molecular representations, applied to more than 2, 700 QSAR problems.
OpenML Benchmarking Suites
Machine learning research depends on objectively interpretable, comparable, and reproducible algorithm benchmarks.
OpenML: An R Package to Connect to the Machine Learning Platform OpenML
We show how the OpenML package allows R users to easily search, download and upload data sets and machine learning tasks.
ASlib: A Benchmark Library for Algorithm Selection
To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature.
OpenML: networked science in machine learning
Many sciences have made significant breakthroughs by adopting online tools that help organize, structure and mine information that is too detailed to be printed in journals.
Open science in machine learning
We present OpenML and mldata, open science platforms that provides easy access to machine learning data, software and results to encourage further study and application.
