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Found 9 papers, 4 papers with code
Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach
In this work, we propose a novel hierarchical neural architecture search approach for time series forecasting tasks.
AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks
The fields of both Natural Language Processing (NLP) and Automated Machine Learning (AutoML) have achieved remarkable results over the past years.
Efficient Automated Deep Learning for Time Series Forecasting
In contrast to common NAS search spaces, we designed a novel neural architecture search space covering various state-of-the-art architectures, allowing for an efficient macro-search over different DL approaches.
Searching in the Forest for Local Bayesian Optimization
Because of its sample efficiency, Bayesian optimization (BO) has become a popular approach dealing with expensive black-box optimization problems, such as hyperparameter optimization (HPO).
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Algorithm parameters, in particular hyperparameters of machine learning algorithms, can substantially impact their performance.
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges
Most machine learning algorithms are configured by one or several hyperparameters that must be carefully chosen and often considerably impact performance.
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization
Neural architecture search (NAS) and hyperparameter optimization (HPO) make deep learning accessible to non-experts by automatically finding the architecture of the deep neural network to use and tuning the hyperparameters of the used training pipeline.
Squirrel: A Switching Hyperparameter Optimizer
In this short note, we describe our submission to the NeurIPS 2020 BBO challenge.
