Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …

Neural architecture search (NAS) has emerged as a promising avenue for automatically
designing task-specific neural networks. Existing NAS approaches require one complete …

Most uses of machine learning today involve training a model from scratch for a particular
task, or sometimes starting with a model pretrained on a related task and then fine-tuning on …

Convolutional neural networks (CNNs) have achieved state-of-the-art performance in many
image classification tasks. However, training a deep CNN requires a massive amount of …

The traditional Machine Learning (ML) methodology requires to fragment the development
and experimental process into disconnected iterations whose feedback is used to guide …

While deep neural networks excel in solving visual recognition tasks, they require significant
effort to find hyperparameters that make them work optimally. Hyperparameter Optimization …

As a human choosing a supervised learning algorithm, it is natural to begin by reading a text
description of the dataset and documentation for the algorithms you might use. We …

Анотація For the past years, many researchers and engineers have been developing and
optimising deep neural networks (DNN). The process of neural architecture design and …

Hyperparameter optimization (HPO) methods alleviate the significant effort required to
obtain hyperparameters that perform optimally on visual learning problems. Existing …

A standard ML model is commonly generated by a single method that specifies aspects such
as architecture, initialization, training data and hyperparameters configuration. The …