Our evaluation on NAS-Bench-360 is thus a robustness test that checks whether the massive amount of largely computer vision-driven progress in the field of NAS is actually indicative of wider success of AutoML across a variety of applications, data types, and tasks. we do not include tabular or graph-based data, thus allowing for the application of most NAS methods. At the same time, we constrain all tasks to be amenable to modern NAS search spaces, i.e. These tasks represent a diverse set of signals, including various kinds of imaging sources, simulation data, genomic data, and more. NAS-Bench-360 is a benchmark suite consisting of ten ML tasks that we developed jointly with Renbo Tu, Nick Roberts, Junhong Shen, and Fred Sala. NAS-Bench-360: A NAS Benchmark for diverse tasks You can learn more about getting involved with either of these efforts at the bottom of this post. #VISUAL PARADIGM COMPARISON FREE#Unlike most past competitions in the AutoML community, competitors in the AutoML Decathlon are free (and in fact encouraged) to consider a wide range of approaches from traditional hyperparameter optimization and ensembling methods to modern techniques such as NAS and large-scale transfer learning. During the public development phase of the competition we will release a set of diverse tasks that will be representative of (but distinct from) the final set of test tasks on which evaluation will be performed. The second is a NeurIPS 2022 competition (which we are soft-launching today!) that builds on our NAS-Bench-360 work yet has a broader vision of understanding what is truly the best approach for a practitioner to take when faced with a modern ML problem. Specifically, the 10 tasks vary in their domain (including image, finance time series, audio, and natural sciences), problem type (including regression, single-label, and multi-label classification), and scale (ranging from several thousands to hundreds of thousands of observations). 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. Over the past decade, machine learning (ML) has grown rapidly in both popularity and complexity.
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