Results 1 to 10 of about 7,560,832 (284)

Array programming with NumPy [PDF]

Nature, 2020
Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language.
Charles R. Harris, K. Millman, S. Walt, R. Gommers, Pauli Virtanen, D. Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, M. Kerkwijk, M. Brett, A. Haldane, Jaime Fern'andez del R'io, Marcy Wiebe, Pearu Peterson, Pierre G'erard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, C. Gohlke, T. Oliphant   +25 more
semanticscholar   +1 more source

CodeBERT: A Pre-Trained Model for Programming and Natural Languages [PDF]

Findings, 2020
We present CodeBERT, a bimodal pre-trained model for programming language (PL) and natural language (NL). CodeBERT learns general-purpose representations that support downstream NL-PL applications such as natural language code search, code documentation ...
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, Ming Zhou   +10 more
semanticscholar   +1 more source

Decoding by linear programming [PDF]

IEEE Transactions on Information Theory, 2005
This paper considers a natural error correcting problem with real valued input/output. We wish to recover an input vector f/spl isin/R/sup n/ from corrupted measurements y=Af+e.
E. Candès, T. Tao
semanticscholar   +1 more source

Stan: A Probabilistic Programming Language.

Journal of Statistical Software, 2017
Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants.
Bob Carpenter, A. Gelman, M. Hoffman, Daniel Lee, Ben Goodrich, M. Betancourt, Marcus A. Brubaker, Jiqiang Guo, Peter Li, A. Riddell   +9 more
semanticscholar   +1 more source

Dynamic Programming

Wiley Encyclopedia of Computer Science and Engineering, 2018
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Ryuichi Oka, Aizu Wakamatsu
semanticscholar   +1 more source

Probabilistic programming in Python using PyMC3

PeerJ Computer Science, 2016
Probabilistic Programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models.
J. Salvatier, Thomas V. Wiecki, C. Fonnesbeck   +2 more
semanticscholar   +1 more source

Nonlinear Programming

Mathematical Programming Methods for Geographers and Planners, 2021
James Killen
semanticscholar   +1 more source

The Child in America: Behavior Problems and Programs [PDF]

, 1929
Phyllis Blanchard, W. I. Thomas, Dorothy Swaine Thomas   +2 more
openalex   +1 more source

Summer Meeting Program [PDF]

, 1940

openalex   +1 more source

Rabies Problems and Control: A Nation-Wide Program [PDF]

, 1949
James H. Steele, Ernest S. Tierkel
openalex   +1 more source