SciPy: Open Source Scientific Tools for Python

By E. Jones, T. Oliphant, Pearu Peterson. 2001

9,638 citations recorded by the source corpus.

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

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In ML Systems & Hardware

In Numerical Methods & Scientific Computing

Citations in the corpus

Theano: new features and speed improvements (2012, methodology): “Both have access to a Matlab-like environment: Torch7 includes modules for tensor manipulations and plotting, while Theano benefits from various external Python libraries to perform those tasks (notably SciPy (Jones et al., 2001), NumPy (Oliphant, 2007), matplotlib (Hunter, 2007), IPython (Pérez and”
Revisiting Self-Supervised Visual Representation Learning (2019, methodology): “Training linear models with L-BFGS: We use a publicly available implementation of the L-BFGS algorithm [6] from the scipy [4] package with the default parameters and set the maximum number of updates to 800.”
Theano: A Python framework for fast computation of mathematical expressions (2016, methodology): “The code to compute output values given input values for each Op can be implemented either in Python or in C++ (or CUDA for GPU Ops), using the C API from Python and NumPy (and from CudaNdarray or GpuArray for GPU).”
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization (2018, methodology): “For the optimal 4 loss, we used scipy.optimize.minimize – a numerical optimizer built into SciPy (Jones et al., 2001–), with the default method of BFGS (Nocedal, 1980).”
Detecting Violations of Differential Privacy (2018, methodology): “2Here we use a notation from SciPy [25] package where Hypergeom.”
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, methodology): “tem attributes are determined by applying a binarizing threshold to α i inequation (3) such that every item has at least one attribute. User representations are then learned using scipy.optimize.nnls [30]. system, whereas the collaborative filtering approach determines this collection of users based”
Statistical Arbitrage in Cryptocurrency Markets (2019, methodology): “Furthermore, the package sci-kit learn (Pedregosa et al. 2011) is used for the random forest and logistic regression model and the packages SciPy (Jones et al. 2014) and Empyrical (Quantopian Inc. 2016) are deployed for the calculation of the statistical properties and performance analysis of the re”
Quantitative missense variant effect prediction using large-scale mutagenesis data (2017, neutral): “…https://www.python.org/ Numpy/1.13.1 van der Walt et al. (2011) http://www.numpy.org/ GraphLab/2.1 Low et al. (2012) https://turi.com/ Scipy/0.19.1 Jones et al. (2001) https://www.scipy.org/ scikit-learn/0.17.0 Pedregosa et al. (2011) http://scikit-learn.org/stable/ R version 3.2.3 R…”
Garbage in, garbage out?: do machine learning application papers in social computing report where human-labeled training data comes from? (2019, positive): “All computational analysis and scripting was conducted in Python 3.7 [65], using the following libraries: Pandas dataframes [36] for data parsing and transformation; SciPy [28] and NumPy [64] for quantitative computations; and Matplotlib [25] and Seaborn [67] for visualization.”
SymPy: Symbolic computing in Python (2017, background): “Internally these matrices store the elements as Lists of Lists (LIL) (Jones et al., 2001), meaning the matrix is stored as a list of lists of entries (effectively, the input format used to create the matrix A above), making it a dense representation.”