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Array Programming with NumPy

Array Programming with NumPy

18 June 2020
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 Fernández del Río
Marcy Wiebe
Pearu Peterson
Pierre Gérard-Marchant
Kevin Sheppard
Tyler Reddy
Warren Weckesser
Hameer Abbasi
C. Gohlke
T. Oliphant
ArXiv (abs)PDFHTML

Papers citing "Array Programming with NumPy"

39 / 1,339 papers shown
Title
Predicting Hydroxyl Mediated Nucleophilic Degradation and Molecular
  Stability of RNA Sequences through the Application of Deep Learning Methods
Predicting Hydroxyl Mediated Nucleophilic Degradation and Molecular Stability of RNA Sequences through the Application of Deep Learning Methods
Ankita Singhal
16
4
0
09 Nov 2020
Reverse engineering learned optimizers reveals known and novel
  mechanisms
Reverse engineering learned optimizers reveals known and novel mechanisms
Niru Maheswaranathan
David Sussillo
Luke Metz
Ruoxi Sun
Jascha Narain Sohl-Dickstein
97
22
0
04 Nov 2020
PyLightcurve-torch: a transit modelling package for deep learning
  applications in PyTorch
PyLightcurve-torch: a transit modelling package for deep learning applications in PyTorch
M. Morvan
A. Tsiaras
N. Nikolaou
Ingo P. Waldmann
78
13
0
03 Nov 2020
Aspectuality Across Genre: A Distributional Semantics Approach
Aspectuality Across Genre: A Distributional Semantics Approach
Thomas Kober
Malihe Alikhani
Matthew Stone
Mark Steedman
37
12
0
31 Oct 2020
Learning to Represent Action Values as a Hypergraph on the Action
  Vertices
Learning to Represent Action Values as a Hypergraph on the Action Vertices
Arash Tavakoli
Mehdi Fatemi
Petar Kormushev
74
23
0
28 Oct 2020
Iterative Amortized Policy Optimization
Iterative Amortized Policy Optimization
Joseph Marino
Alexandre Piché
Alessandro Davide Ialongo
Yisong Yue
OffRL
117
21
0
20 Oct 2020
On Application of Block Kaczmarz Methods in Matrix Factorization
On Application of Block Kaczmarz Methods in Matrix Factorization
Edwin Chau
Jamie Haddock
37
1
0
20 Oct 2020
Semi-parametric $γ$-ray modeling with Gaussian processes and
  variational inference
Semi-parametric γγγ-ray modeling with Gaussian processes and variational inference
S. Mishra-Sharma
Kyle Cranmer
MedIm
102
7
0
20 Oct 2020
Causal Discovery using Compression-Complexity Measures
Causal Discovery using Compression-Complexity Measures
Pranay Yadav
N. Nagaraj
CML
20
15
0
19 Oct 2020
Enabling Fast Differentially Private SGD via Just-in-Time Compilation
  and Vectorization
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization
P. Subramani
Nicholas Vadivelu
Gautam Kamath
101
83
0
18 Oct 2020
The Deep Bootstrap Framework: Good Online Learners are Good Offline
  Generalizers
The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers
Preetum Nakkiran
Behnam Neyshabur
Hanie Sedghi
OffRL
97
11
0
16 Oct 2020
Physically constrained causal noise models for high-contrast imaging of
  exoplanets
Physically constrained causal noise models for high-contrast imaging of exoplanets
Timothy D. Gebhard
M. Bonse
S. Quanz
Bernhard Schölkopf
27
2
0
12 Oct 2020
fairseq S2T: Fast Speech-to-Text Modeling with fairseq
fairseq S2T: Fast Speech-to-Text Modeling with fairseq
Changhan Wang
Yun Tang
Xutai Ma
Anne Wu
Sravya Popuri
Dmytro Okhonko
J. Pino
VLMLRM
107
276
0
11 Oct 2020
Learning Not to Learn: Nature versus Nurture in Silico
Learning Not to Learn: Nature versus Nurture in Silico
R. T. Lange
Henning Sprekeler
66
10
0
09 Oct 2020
Do Explicit Alignments Robustly Improve Multilingual Encoders?
Do Explicit Alignments Robustly Improve Multilingual Encoders?
Shijie Wu
Mark Dredze
46
7
0
06 Oct 2020
Tasks, stability, architecture, and compute: Training more effective
  learned optimizers, and using them to train themselves
Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves
Luke Metz
Niru Maheswaranathan
C. Freeman
Ben Poole
Jascha Narain Sohl-Dickstein
151
61
0
23 Sep 2020
Distributional Generalization: A New Kind of Generalization
Distributional Generalization: A New Kind of Generalization
Preetum Nakkiran
Yamini Bansal
OOD
80
42
0
17 Sep 2020
Quantifying the Preferential Direction of the Model Gradient in
  Adversarial Training With Projected Gradient Descent
Quantifying the Preferential Direction of the Model Gradient in Adversarial Training With Projected Gradient Descent
Ricardo Bigolin Lanfredi
Joyce D. Schroeder
Tolga Tasdizen
51
12
0
10 Sep 2020
Independent Vector Analysis via Log-Quadratically Penalized Quadratic
  Minimization
Independent Vector Analysis via Log-Quadratically Penalized Quadratic Minimization
Robin Scheibler
72
11
0
23 Aug 2020
Estimating Causal Effects with the Neural Autoregressive Density
  Estimator
Estimating Causal Effects with the Neural Autoregressive Density Estimator
Sergio Garrido
S. Borysov
Jeppe Rich
Francisco Câmara Pereira
CML
51
7
0
17 Aug 2020
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle
  Reconstruction in High Energy Physics
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics
Y. Iiyama
G. Cerminara
Abhijay Gupta
J. Kieseler
Vladimir Loncar
...
Miaoyuan Liu
K. Pedro
N. Tran
E. Kreinar
Zhenbin Wu
75
68
0
08 Aug 2020
Bringing UMAP Closer to the Speed of Light with GPU Acceleration
Bringing UMAP Closer to the Speed of Light with GPU Acceleration
Corey J. Nolet
V. Lafargue
Edward Raff
Thejaswi Nanditale
Tim Oates
John Zedlewski
Joshua Patterson
77
35
0
01 Aug 2020
Uncovering the structure of clinical EEG signals with self-supervised
  learning
Uncovering the structure of clinical EEG signals with self-supervised learning
Hubert J. Banville
O. Chehab
Aapo Hyvarinen
Denis A. Engemann
Alexandre Gramfort
94
198
0
31 Jul 2020
ResNet After All? Neural ODEs and Their Numerical Solution
ResNet After All? Neural ODEs and Their Numerical Solution
Katharina Ott
P. Katiyar
Philipp Hennig
Michael Tiemann
88
31
0
30 Jul 2020
Coarse Graining Molecular Dynamics with Graph Neural Networks
Coarse Graining Molecular Dynamics with Graph Neural Networks
B. Husic
N. Charron
Dominik Lemm
Jiang Wang
Adria Pérez
...
Yaoyi Chen
Simon Olsson
Gianni De Fabritiis
Frank Noé
C. Clementi
AI4CE
121
161
0
22 Jul 2020
OccamNet: A Fast Neural Model for Symbolic Regression at Scale
OccamNet: A Fast Neural Model for Symbolic Regression at Scale
Owen Dugan
Rumen Dangovski
Allan dos Santos Costa
Samuel Kim
Pawan Goyal
J. Jacobson
M. Soljavcić
95
11
0
16 Jul 2020
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing
  Flows
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows
H. M. Dolatabadi
S. Erfani
C. Leckie
AAML
123
66
0
15 Jul 2020
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
Matthias Feurer
Katharina Eggensperger
Stefan Falkner
Marius Lindauer
Frank Hutter
113
285
0
08 Jul 2020
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs
Ignavier Ng
AmirEmad Ghassami
Kun Zhang
CML
70
189
0
17 Jun 2020
Categorical Stochastic Processes and Likelihood
Categorical Stochastic Processes and Likelihood
Dan Shiebler
39
9
0
10 May 2020
Distilling Spikes: Knowledge Distillation in Spiking Neural Networks
Distilling Spikes: Knowledge Distillation in Spiking Neural Networks
R. K. Kushawaha
S. Kumar
Biplab Banerjee
R. Velmurugan
43
33
0
01 May 2020
giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and
  Data Exploration
giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration
Guillaume Tauzin
Umberto Lupo
Lewis Tunstall
Julián Burella Pérez
Matteo Caorsi
Wojciech Reise
A. Medina-Mardones
A. Dassatti
K. Hess
AI4CE
60
192
0
06 Apr 2020
Statistical power for cluster analysis
Statistical power for cluster analysis
Edwin S. Dalmaijer
Camilla L. Nord
D. Astle
28
298
0
01 Mar 2020
Time Series Alignment with Global Invariances
Time Series Alignment with Global Invariances
Titouan Vayer
R. Tavenard
Laetitia Chapel
Nicolas Courty
Rémi Flamary
Yann Soullard
AI4TS
83
17
0
10 Feb 2020
OpenML-Python: an extensible Python API for OpenML
OpenML-Python: an extensible Python API for OpenML
Matthias Feurer
Jan N. van Rijn
Arlind Kadra
Pieter Gijsbers
Neeratyoy Mallik
Sahithya Ravi
Andreas Müller
Joaquin Vanschoren
Frank Hutter
ELMGP
99
91
0
06 Nov 2019
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic
  Bayesian Optimisation
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation
Ryan-Rhys Griffiths
Alexander A. Aldrick
Miguel García-Ortegón
Vidhi R. Lalchand
A. Lee
101
35
0
17 Oct 2019
GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on
  the GPU
GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU
Carl Yang
A. Buluç
John Douglas Owens
GNN
46
101
0
04 Aug 2019
Finding Moments in Video Collections Using Natural Language
Finding Moments in Video Collections Using Natural Language
Victor Escorcia
Mattia Soldan
Josef Sivic
Guohao Li
Bryan C. Russell
48
7
0
30 Jul 2019
ABCpy: A High-Performance Computing Perspective to Approximate Bayesian
  Computation
ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
Ritabrata Dutta
Marcel Schoengens
Lorenzo Pacchiardi
Avinash Ummadisingu
Nicole Widmer
Pierre Künzli
J. Onnela
Antonietta Mira
65
25
0
13 Nov 2017
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