Guide to NumPy

By T. Oliphant. 2015

1,985 citations recorded by the source corpus.

Subfields: ML Systems & Hardware, Numerical Methods & Scientific Computing

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Citations in the corpus

Input Convex Neural Networks (2016, methodology): “The full source code for all experiments is available in the icml2017 branch at https://github.com/ locuslab/icnn and our implementation is built using Python (Van Rossum & Drake Jr, 1995) with the numpy (Oliphant, 2006) and TensorFlow (Abadi et al., 2016) packages.”
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems (2019, neutral): “, 2016), Numpy (Oliphant, 2006), Pandas (McKinney et al.”
Energy flow networks: deep sets for particle jets (2018, neutral): “Keras requires contiguous NumPy [128] arrays as input, so the events are padded with all-zero particles to ensure they have equal length.”
Comparing Implementations of Estimation Methods for Spatial Econometrics (2015, methodology): “In MATLAB, use was made of the analogous save function; in Stata the file command with write binary options was used; in Python save imported from NumPy (Oliphant 2006).”
Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond (2020, methodology): “The cupy [109] backend constitutes an alternative to the CPU-bound numpy [110] package, providing optional support for modern GPU hardware from NVIDIA.”
Two Remarkably Luminous Galaxy Candidates at z ≈ 10–12 Revealed by JWST (2022, neutral): “Facilities: JWST, HST Software: IPython (Pérez & Granger 2007), matplotlib (Hunter 2007), numpy (Oliphant 2015), scipy (Virtanen et al. 2020), jupyter (Kluyver et al. 2016), Astropy (Astropy”
Binary Neutron Star Mergers: Mass Ejection, Electromagnetic Counterparts, and Nucleosynthesis (2018, positive): “Software: WhiskyTHC (Radice et al. 2012), Lorene (Gourgoulhon et al. 1999), Matplotlib (Hunter 2007), NumPy (Oliphant 2015), SciPy (Jones et al. 2001), Pandas (McKinney 2010), iPython (Perez & Granger 2007), VisIt (Childs et al. 2012).”
Triangulating Python Performance Issues with Scalene (2022, methodology): “These libraries include the NumPy numeric library [25], the machine learning libraries SciKit-Learn [29] and TensorFlow [2,3], among many others.”
Positive-Unlabeled Reward Learning (2019, methodology): “We would like to thank the scientific python community for developing the core set of tools that enabled this work, including Tensorflow [36], Numpy [37], Pandas [38], and Matplotlib [39].”
Predictions of the WFIRST Microlensing Survey. I. Bound Planet Detection Rates (2018, background): “This mass function is based on the power-law bound planet mass function measured by Cassan et al. (2012) from microlensing observations, but saturated at a value of 2 planets per star below a mass of 5.”
Chainer : a Next-Generation Open Source Framework for Deep Learning (2015, methodology): “Python was chosen as the programming language for Chainer due to the simplicity, popularity, and beneﬁts of using the existing multi-dimensional array library (NumPy [17]).”