With evaluations on ten diverse tasks-including a precomputed tabular benchmark on three of them-NAS-Bench-360 is the first NAS testbed that goes beyond traditional AI domains such as vision, text, and audio signals. The first is a benchmark suite focusing on the burgeoning field of neural architecture search (NAS), which seeks to automate the development of neural network models. This blog post is dedicated to two recent but related efforts that measure the field’s current effectiveness at achieving this goal: NAS-Bench-360 and the AutoML Decathlon. Here we ask about the current status of AutoML, namely: can available AutoML tools quickly and painlessly attain near-expert performance on diverse learning tasks? Given that such resource intensive iteration is expensive and inaccessible to most practitioners, AutoML has emerged with an overarching goal of enabling any team of ML developers to deploy ML on arbitrary new tasks. However, progress in such areas has often required expert-driven development of complex neural network architectures, expensive hyperparameter tuning, or both. ![]() Driven by advances in deep neural networks, ML is now being applied far beyond its traditional domains like computer vision and text processing, with applications in areas as diverse as solving partial differential equations (PDEs), tracking credit card fraud, and predicting medical conditions from gene sequences. These data establish the impact of circadian rhythmicity and sex on waking cognition and have implications for understanding the regulation of brain function, cognition, and affect in shift-work, jetlag, and aging.Over the past decade, machine learning (ML) has grown rapidly in both popularity and complexity. The sex differences in the circadian modulation of cognition could not be explained by sex differences in the circadian amplitude of plasma melatonin and electroencephalographic slow-wave activity. The largest circadian modulation was observed for effort, whereas accuracy exhibited the largest sex difference in circadian modulation. Principal components analysis of the performance measures yielded three factors, accuracy, effort, and speed, which reflect core performance characteristics in a range of cognitive tasks and therefore are likely to be important for everyday performance. The amplitude of the circadian modulation was larger in women in 11 of 39 performance measures so that their performance was more impaired in the early morning hours. ![]() ![]() Although these effects were seen in both men and women, there were quantitative differences. The largest circadian effects were observed for reported sleepiness, mood, and reported effort the effects on working memory and temporal processing were smaller. We examined the circadian and sleep–wake-dependent regulation of cognition in 16 men and 18 women in a forced desynchrony protocol and quantified the separate contributions of circadian phase, prior sleep, and elapsed time awake on cognition and sleep. The sleep–wake cycle and circadian rhythmicity both contribute to brain function, but whether this contribution differs between men and women and how it varies across cognitive domains and subjective dimensions has not been established.
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