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Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms
Convolutional Neural Networks (CNNs) have proven to be a powerful state-...
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Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models
Bayesian Optimization is a popular tool for tuning algorithms in automat...
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Theory-based Habit Modeling for Enhancing Behavior Prediction
Psychological theories of habit posit that when a strong habit is formed...
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Aerial Imagery Pixel-level Segmentation
Aerial imagery can be used for important work on a global scale. Neverth...
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Importance of Tuning Hyperparameters of Machine Learning Algorithms
The performance of many machine learning algorithms depends on their hyp...
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GAMA: a General Automated Machine learning Assistant
The General Automated Machine learning Assistant (GAMA) is a modular Aut...
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Adaptation Strategies for Automated Machine Learning on Evolving Data
Automated Machine Learning (AutoML) systems have been shown to efficient...
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OpenML-Python: an extensible Python API for OpenML
OpenML is an online platform for open science collaboration in machine l...
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An Open Source AutoML Benchmark
In recent years, an active field of research has developed around automa...
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A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers
For many machine learning algorithms, predictive performance is critical...
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SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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An empirical study on hyperparameter tuning of decision trees
Machine learning algorithms often contain many hyperparameters whose val...
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Transformative Machine Learning
The key to success in machine learning (ML) is the use of effective data...
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Meta-Learning: A Survey
Meta-learning, or learning to learn, is the science of systematically ob...
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Towards Reproducible Empirical Research in Meta-Learning
Meta-learning is increasingly used to support the recommendation of mach...
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ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies
The ML-Schema, proposed by the W3C Machine Learning Schema Community Gro...
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Layered TPOT: Speeding up Tree-based Pipeline Optimization
With the demand for machine learning increasing, so does the demand for ...
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Meta-QSAR: a large-scale application of meta-learning to drug design and discovery
We investigate the learning of quantitative structure activity relations...
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OpenML Benchmarking Suites and the OpenML100
We advocate the use of curated, comprehensive benchmark suites of machin...
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OpenML: An R Package to Connect to the Machine Learning Platform OpenML
OpenML is an online machine learning platform where researchers can easi...
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ASlib: A Benchmark Library for Algorithm Selection
The task of algorithm selection involves choosing an algorithm from a se...
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