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Nathaniel J. Smith
Researcher at University of California, San Diego
Publications - 21
Citations - 13427
Nathaniel J. Smith is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Word recognition & Psycholinguistics. The author has an hindex of 13, co-authored 21 publications receiving 3980 citations. Previous affiliations of Nathaniel J. Smith include University of Edinburgh.
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Journal ArticleDOI
Array programming with NumPy
Charles R. Harris,K. Jarrod Millman,Stefan van der Walt,Stefan van der Walt,Ralf Gommers,Pauli Virtanen,David Cournapeau,Eric Wieser,Julian Taylor,Sebastian Berg,Nathaniel J. Smith,Robert Kern,Matti Picus,Stephan Hoyer,Marten H. van Kerkwijk,Matthew Brett,Matthew Brett,Allan Haldane,Jaime Fernández del Río,Mark Wiebe,Mark Wiebe,Pearu Peterson,Pierre Gérard-Marchant,Kevin Sheppard,Tyler Reddy,Warren Weckesser,Hameer Abbasi,Christoph Gohlke,Travis E. Oliphant +28 more
TL;DR: In this paper, the authors review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data, and their evolution into a flexible interoperability layer between increasingly specialized computational libraries is discussed.
Journal ArticleDOI
Array Programming with NumPy
Charles R. Harris,K. Jarrod Millman,Stefan van der Walt,Stefan van der Walt,Ralf Gommers,Pauli Virtanen,David Cournapeau,Eric Wieser,Julian Taylor,Sebastian Berg,Nathaniel J. Smith,Robert Kern,Matti Picus,Stephan Hoyer,Marten H. van Kerkwijk,Matthew Brett,Matthew Brett,Allan Haldane,Jaime Fernández del Río,Mark Wiebe,Mark Wiebe,Pearu Peterson,Pierre Gérard-Marchant,Kevin Sheppard,Tyler Reddy,Warren Weckesser,Hameer Abbasi,Christoph Gohlke,Travis E. Oliphant +28 more
TL;DR: How a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data is reviewed.
Journal ArticleDOI
The effect of word predictability on reading time is logarithmic
Nathaniel J. Smith,Roger Levy +1 more
TL;DR: A state-of-the-art computational language model, two large behavioral data-sets, and non-parametric statistical techniques are combined to establish for the first time the quantitative form of the relationship between expectation and reading times, finding that it is logarithmic over six orders of magnitude in estimated predictability.
Book ChapterDOI
A look around at what lies ahead: Prediction and predictability in language processing.
TL;DR: It is argued for the importance of investigating such linguistic prediction as yet another example of a neural system in which probability estimation is inherent, with a proposal to move beyond the debate of whether there is linguistic prediction, toward focusing research on how pre-activation may occur and what is pre-activated.