Add to ORKGHyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance. While a large number of methods for Hyperparameter Optimization (HPO) have been developed, their incurred costs are often untenable for modern DL. Consequently, manual experimentation is still the most prevalent approach to optimize hyperparameters, relying on the researcher's intuition, domain knowledge, and cheap preliminary explorations. To resolve this misalignment between HPO algorithms and DL researchers, we propose PriorBand, an HPO algorithm tailored to DL, able to utilize both expert beliefs and cheap proxy tasks. Empirically, we demonstrate PriorBand's efficiency across a range of DL benchmarks and show its gains under informative expert ...
As a result of the ever increasing complexity of configuring and fine-tuning machine learning models...
National audience<p>One common problem in building deep learning architectures is the choice of the ...
Part I: Theory - Basics of Hyperparameter Optimization - Exhausive Searches - Surrogate-based Op...
Deep Neural Networks have advanced rapidly over the past several years. However, it still seems like...
The performance of optimizers, particularly in deep learning, depends considerably on their chosen h...
Most machine learning algorithms are configured by a set of hyperparameters whose values must be car...
Hyperparameter tuning is an integral part of deep learning research. Finding hyperparameter values t...
Hyperparameter optimization (HPO) is a fundamental problem in automatic machine learning (AutoML). H...
Hyperparameters in machine learning (ML) have received a fair amount of attention, and hyperparamete...
Automatically searching for optimal hyperparameter configurations is of crucial importance for apply...
Hyperparameter optimization (HPO) is crucial for fine-tuning machine learning models but can be comp...
Neural networks have emerged as a powerful and versatile class of machine learning models, revolutio...
Deep neural networks have recently become astonishingly successful at many machine learning problems...
Hyperparameter optimization in machine learning is a critical task that aims to find the hyper-param...
Deep learning is proving to be a useful tool in solving problems from various domains. Despite a ric...
As a result of the ever increasing complexity of configuring and fine-tuning machine learning models...
National audience<p>One common problem in building deep learning architectures is the choice of the ...
Part I: Theory - Basics of Hyperparameter Optimization - Exhausive Searches - Surrogate-based Op...
Deep Neural Networks have advanced rapidly over the past several years. However, it still seems like...
The performance of optimizers, particularly in deep learning, depends considerably on their chosen h...
Most machine learning algorithms are configured by a set of hyperparameters whose values must be car...
Hyperparameter tuning is an integral part of deep learning research. Finding hyperparameter values t...
Hyperparameter optimization (HPO) is a fundamental problem in automatic machine learning (AutoML). H...
Hyperparameters in machine learning (ML) have received a fair amount of attention, and hyperparamete...
Automatically searching for optimal hyperparameter configurations is of crucial importance for apply...
Hyperparameter optimization (HPO) is crucial for fine-tuning machine learning models but can be comp...
Neural networks have emerged as a powerful and versatile class of machine learning models, revolutio...
Deep neural networks have recently become astonishingly successful at many machine learning problems...
Hyperparameter optimization in machine learning is a critical task that aims to find the hyper-param...
Deep learning is proving to be a useful tool in solving problems from various domains. Despite a ric...
As a result of the ever increasing complexity of configuring and fine-tuning machine learning models...
National audience<p>One common problem in building deep learning architectures is the choice of the ...
Part I: Theory - Basics of Hyperparameter Optimization - Exhausive Searches - Surrogate-based Op...