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Jacob VanderPlas
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2020 – today
- 2020
- [i9]J. Bryce Kalmbach, Jacob VanderPlas, Andrew J. Connolly:
Applying Information Theory to Design Optimal Filters for Photometric Redshifts. CoRR abs/2001.01372 (2020)
2010 – 2019
- 2019
- [i8]Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright
, Stéfan van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, Ilhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald
SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python. CoRR abs/1907.10121 (2019) - 2018
- [j10]Juan B. Cabral
From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning. Astron. Comput. 25: 213-220 (2018) - [j9]David P. Fleming
approxposterior: Approximate Posterior Distributions in Python. J. Open Source Softw. 3(29): 781 (2018) - [j8]Jacob VanderPlas
Altair: Interactive Statistical Visualizations for Python. J. Open Source Softw. 3(32): 1057 (2018) - [j7]Arfon M. Smith, Kyle E. Niemeyer, Daniel S. Katz
Journal of Open Source Software (JOSS): design and first-year review. PeerJ Comput. Sci. 4: e147 (2018) - [d2]Jacob VanderPlas
Altair: Declarative statistical visualization library for Python. Version 2. Zenodo, 2018 [all versions] - [d1]Jacob VanderPlas
Altair: Declarative statistical visualization library for Python. Version 2.3. Zenodo, 2018 [all versions] - [i7]Juan B. Cabral
From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning. CoRR abs/1809.02154 (2018) - 2017
- [j6]Parmita Mehta, Sven Dorkenwald, Dongfang Zhao, Tomer Kaftan, Alvin Cheung
Comparative Evaluation of Big-Data Systems on Scientific Image Analytics Workloads. Proc. VLDB Endow. 10(11): 1226-1237 (2017) - [i6]Arfon M. Smith, Kyle E. Niemeyer, Daniel S. Katz, Lorena A. Barba, George Githinji, Melissa Gymrek, Kathryn D. Huff, Christopher R. Madan, Abigail Cabunoc Mayes, Kevin Mattheus Moerman
Journal of Open Source Software (JOSS): design and first-year review. CoRR abs/1707.02264 (2017) - [i5]Daniela Huppenkothen, Anthony Arendt, David W. Hogg, Karthik Ram, Jake VanderPlas, Ariel Rokem:
Hack Weeks as a model for Data Science Education and Collaboration. CoRR abs/1711.00028 (2017) - 2016
- [j5]James McQueen, Marina Meila, Jacob VanderPlas, Zhongyue Zhang:
Megaman: Scalable Manifold Learning in Python. J. Mach. Learn. Res. 17: 148:1-148:5 (2016) - [j4]Jake VanderPlas
mst_clustering: Clustering via Euclidean Minimum Spanning Trees. J. Open Source Softw. 1(1): 12 (2016) - [i4]James McQueen, Marina Meila, Jacob VanderPlas, Zhongyue Zhang:
megaman: Manifold Learning with Millions of points. CoRR abs/1603.02763 (2016) - [i3]Parmita Mehta, Sven Dorkenwald, Dongfang Zhao, Tomer Kaftan, Alvin Cheung, Magdalena Balazinska, Ariel Rokem, Andrew J. Connolly, Jacob VanderPlas, Yusra AlSayyad:
Comparative Evaluation of Big-Data Systems on Scientific Image Analytics Workloads. CoRR abs/1612.02485 (2016) - 2014
- [c3]Jake VanderPlas:
Frequentism and Bayesianism: A Python-driven Primer. SciPy 2014: 85-93 - 2013
- [j3]Jacob VanderPlas, Emad Soroush, K. Simon Krughoff, Magdalena Balazinska:
Squeezing a Big Orange into Little Boxes: The AscotDB System for Parallel Processing of Data on a Sphere. IEEE Data Eng. Bull. 36(4): 11-20 (2013) - [j2]Matthew I. Moyers, Emad Soroush, Spencer Wallace, K. Simon Krughoff, Jake VanderPlas
A Demonstration of Iterative Parallel Array Processing in Support of Telescope Image Analysis. Proc. VLDB Endow. 6(12): 1322-1325 (2013) - [i2]Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, Gaël Varoquaux:
API design for machine learning software: experiences from the scikit-learn project. CoRR abs/1309.0238 (2013) - 2012
- [c2]Jacob VanderPlas
Introduction to astroML: Machine learning for astrophysics. CIDU 2012: 47-54 - [i1]Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake VanderPlas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Edouard Duchesnay:
Scikit-learn: Machine Learning in Python. CoRR abs/1201.0490 (2012) - 2011
- [j1]Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake VanderPlas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Edouard Duchesnay:
Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 12: 2825-2830 (2011) - [c1]Liang Xiong, Barnabás Póczos, Jeff G. Schneider, Andrew J. Connolly, Jake VanderPlas:
Hierarchical Probabilistic Models for Group Anomaly Detection. AISTATS 2011: 789-797
Coauthor Index
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