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The journal was created in response to the machine learning explosion of the 2010s. It launched in January 2019, and its opening was met with controversy and boycotts within the machine learning research community due to opposition to Nature publishing the journal as closed access. [2]
This is a list of datasets for machine learning research. It is part of the list of datasets for machine-learning research. These datasets consist primarily of images or videos for tasks such as object detection, facial recognition, and multi-label classification.
Machine Learning is a peer-reviewed scientific journal, published since 1986. In 2001, forty editors and members of the editorial board of Machine Learning resigned in order to support the Journal of Machine Learning Research (JMLR), saying that in the era of the internet, it was detrimental for researchers to continue publishing their papers ...
This is a list of journals published by Nature Research. These include the flagship Nature journal, the Nature Reviews series (which absorbed the former Nature Clinical Practice series in 2009), the npj series, Scientific Reports and many others.
Nature's journal impact factor carries a long tail. [38] Studies of methodological quality and reliability have found that some high-prestige journals including Nature "publish significantly substandard structures", and overall "reliability of published research works in several fields may be decreasing with increasing journal rank". [39]
Pages in category "Nature Research academic journals" The following 169 pages are in this category, out of 169 total. This list may not reflect recent changes .
Physics-informed neural networks for solving Navier–Stokes equations. Physics-informed neural networks (PINNs), [1] also referred to as Theory-Trained Neural Networks (TTNs), [2] are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs).
OpenML: [493] Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms. PMLB: [494] A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms ...