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The need to approximate functions is ubiquitous in science, either due to empirical constraints or high computational cost of accessing the function.
A two-dimensional interpolation function for irregularly-spaced data
Donald Shepard · 1968
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Multiquadric equations of topography and other irregular surfaces
Rolland L Hardy · 1971
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Multidimensional binary search trees used for associative searching
Jon Louis Bentley · 1975
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A critical comparison of some methods for interpolation of scattered data
Richard Franke · 1979
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One-loop corrections for e+ e- annihilation into μ \mu + μ \mu - in the Weinberg model
Giampiero Passarino and Martinus Veltman · 1979
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Two algorithms for constructing a Delaunay triangulation
Der-Tsai Lee and Bruce J Schachter · 1980
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Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
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Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
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Interpolation by regularized spline with tension: II. Application to terrain modeling and surface geometry analysis
Helena Mitášová and Jaroslav Hofierka · 1993
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Sliq: A fast scalable classifier for data mining
Manish Mehta, Rakesh Agrawal, and Jorma Rissanen · 1996
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Comparison of kriging and inverse-distance methods for mapping soil parameters
Carol A Gotway, Richard B Ferguson, Gary W Hergert, and Todd A Peterson · 1996
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Generalized recurrence relations for two-loop propagator integrals with arbitrary masses
Oleg V Tarasov · 1997
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Interpolation methods comparison
C Caruso and F Quarta · 1998
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A comparative study of interpolation methods for mapping soil properties
Alexandra Kravchenko and Donald G Bullock · 1999
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CRC standard probability and statistics tables and formulae
Daniel Zwillinger and Stephen Kokoska · 1999
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Reduction of one-loop tensor 5-point integrals
Ansgar Denner and S Dittmaier · 2003
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Precision electroweak measurements on the Z resonance
The SLD Electroweak, Heavy Flavour Groups, ALEPH Collaboration, DELPHI Collaboration, L3 Collaboration, OPAL Collaboration, SLD Collaboration, LEP Electroweak Working Group, et al · 2006
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Gaussian processes for machine learning
Christopher K Williams and Carl Edward Rasmussen · 2006
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Reduction schemes for one-loop tensor integrals
Ansgar Denner and Stefan Dittmaier · 2006
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TSIL: a program for the calculation of two-loop self-energy integrals
Stephen P Martin and David G Robertson · 2006
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Interpolation method for adapting reduced-order models and application to aeroelasticity
David Amsallem and Charbel Farhat · 2008
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Multi-linear interpolation
Rick Wagner · 2008
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Variational learning of inducing variables in sparse Gaussian processes
Michalis Titsias · 2009
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A comparison of spatial interpolation methods for estimation of average electromagnetic field magnitude
Marco A Azpurua and Karina Dos Ramos · 2010
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A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors
Jin Li and Andrew D Heap · 2011
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Scikit-learn: Machine learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Scalar one-loop 4-point integrals
Ansgar Denner and Stefan Dittmaier · 2011
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Comparison of commonly used image interpolation methods
Dianyuan Han · 2013
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GPflow: A Gaussian process library using TensorFlow
Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke. Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, and James Hensman · 2017
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Collier: a fortran-based complex one-loop library in extended regularizations
Ansgar Denner, Stefan Dittmaier, and Lars Hofer · 2017
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Machine-learning interpolation of population-synthesis simulations to interpret gravitational-wave observations: A case study
Kaze WK Wong and Davide Gerosa · 2019
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(Machine) Learning amplitudes for faster event generation
Fady Bishara and Marc Montull · 2019
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Zaharid/GPTree: Interpolation library using Gaussian Processes and KDTrees, December 2019
Zahari Kassabov · 2019
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James Hensman, Nicolo Fusi, and Neil D Lawrence · 2013
Cited alongside, same era.
Spatial interpolation methods applied in the environmental sciences: A review
Jin Li and Andrew D Heap · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Kurtosis as peakedness, 1905–2014. RIP
Peter H Westfall · 2014
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Xgboost: extreme gradient boosting
Tianqi Chen, Tong He, Michael Benesty, Vadim Khotilovich, Yuan Tang, Hyunsu Cho, et al · 2015
Cited alongside, same era.
Single-jet inclusive rates with exact color at 𝒪 ( α s 4 ) \mathcal{O}(\alpha_{s}^{4})
Michał Czakon, Andreas van Hameren, Alexander Mitov, and Rene Poncelet · 2019
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Deepxs: fast approximation of mssm electroweak cross sections at nlo
Sydney Otten, Krzysztof Rolbiecki, Sascha Caron, Jong-Soo Kim, Roberto Ruiz de Austri, and Jamie Tattersall · 2020
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Using neural networks for efficient evaluation of high multiplicity scattering amplitudes
Simon Badger and Joseph Bullock · 2020
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Xsec: the cross-section evaluation code
Andy Buckley, Anders Kvellestad, Are Raklev, Pat Scott, Jon Vegard Sparre, Jeriek Van den Abeele, and Ingrid A Vazquez-Holm · 2020
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NNLO QCD corrections to three-photon production at the LHC
Herschel A Chawdhry, Michal Czakon, Alexander Mitov, and Rene Poncelet · 2020
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SciPy 1.0: fundamental algorithms for scientific computing in Python
Pauli Virtanen, Ralf Gommers, Travis E Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, et al · 2020
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astropy/photutils: 1.0.0, September 2020
Larry Bradley, Brigitta Sipőcz, Thomas Robitaille, Erik Tollerud, Zè Vinícius, Christoph Deil, Kyle Barbary, Tom J Wilson, Ivo Busko, Hans Moritz Günther, Mihai Cara, Simon Conseil, Azalee Bostroem, Michael Droettboom, E. M. Bray, Lars Andersen Bratholm, P. L. Lim, Geert Barentsen, Matt Craig, Sergio Pascual, Gabriel Perren, Johnny Greco, Axel Donath, Miguel de Val-Borro, Wolfgang Kerzendorf, Yoonsoo P. Bach, Benjamin Alan Weaver, Francesco D’Eugenio, Harrison Souchereau, and Leonardo Ferreira · 2020
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Efficient speed-up of radial basis functions approximation and interpolation formula evaluation
Michal Smolik and Vaclav Skala · 2020
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When Gaussian process meets big data: A review of scalable GPs
Haitao Liu, Yew-Soon Ong, Xiaobo Shen, and Jianfei Cai · 2020
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i-flow: High-dimensional Integration and Sampling with Normalizing Flows
Christina Gao, Joshua Isaacson, and Claudius Krause · 2020
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Targeting Multi-Loop Integrals with Neural Networks
Ramon Winterhalder, Vitaly Magerya, Emilio Villa, Stephen P Jones, Matthias Kerner, Anja Butter, Gudrun Heinrich, and Tilman Plehn · 2021
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A factorisation-aware Matrix element emulator
Daniel Maître and Henry Truong · 2021
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Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
Joseph Aylett-Bullock, Simon Badger, and Ryan Moodie · 2021
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https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.boxplot.html , 2021
matplotlib.pyplot.boxplot — Matplotlib 3.4.3 Documentation · 2021
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Prediction versus truth plots for “function approximation for high-energy physics: Comparing machine learning and interpolation methods”
Ibrahim Chahrour and James D. Wells · 2021
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Ganplifying event samples
Anja Butter, Sascha Diefenbacher, Gregor Kasieczka, Benjamin Nachman, and Tilman Plehn · 2021
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https://lightgbm.readthedocs.io/en/latest/Features.html
Light GBM Documentation · 2026
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