ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1809.09569
  4. Cited By
Tangent: Automatic differentiation using source-code transformation for
  dynamically typed array programming
v1v2 (latest)

Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming

Neural Information Processing Systems (NeurIPS), 2018
25 September 2018
B. V. Merrienboer
D. Moldovan
Alexander B. Wiltschko
ArXiv (abs)PDFHTML

Papers citing "Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming"

11 / 11 papers shown
Title
Auto-Differentiation of Relational Computations for Very Large Scale
  Machine Learning
Auto-Differentiation of Relational Computations for Very Large Scale Machine LearningInternational Conference on Machine Learning (ICML), 2023
Yu-Shuen Tang
Zhimin Ding
Dimitrije Jankov
Binhang Yuan
Daniel Bourgeois
C. Jermaine
BDL
262
7
0
31 May 2023
On the Correctness of Automatic Differentiation for Neural Networks with
  Machine-Representable Parameters
On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable ParametersInternational Conference on Machine Learning (ICML), 2023
Wonyeol Lee
Sejun Park
A. Aiken
PINN
158
6
0
31 Jan 2023
Randomized Automatic Differentiation
Randomized Automatic Differentiation
Deniz Oktay
N. McGreivy
Joshua Aduol
Alex Beatson
Ryan P. Adams
ODL
124
28
0
20 Jul 2020
On Correctness of Automatic Differentiation for Non-Differentiable
  Functions
On Correctness of Automatic Differentiation for Non-Differentiable FunctionsNeural Information Processing Systems (NeurIPS), 2020
Wonyeol Lee
Hangyeol Yu
Xavier Rival
Hongseok Yang
174
44
0
12 Jun 2020
Learning to Control PDEs with Differentiable Physics
Learning to Control PDEs with Differentiable PhysicsInternational Conference on Learning Representations (ICLR), 2020
Philipp Holl
V. Koltun
Nils Thuerey
AI4CEPINN
224
204
0
21 Jan 2020
A Simple Differentiable Programming Language
A Simple Differentiable Programming Language
M. Abadi
G. Plotkin
187
70
0
11 Nov 2019
Deployable probabilistic programming
Deployable probabilistic programmingSIGPLAN symposium on New ideas, new paradigms, and reflections on programming and software (Onward!), 2019
David Tolpin
TPM
174
7
0
20 Jun 2019
Relay: A High-Level Compiler for Deep Learning
Relay: A High-Level Compiler for Deep Learning
Jared Roesch
Steven Lyubomirsky
Marisa Kirisame
Logan Weber
Josh Pollock
Luis Vega
Ziheng Jiang
Tianqi Chen
T. Moreau
Zachary Tatlock
142
21
0
17 Apr 2019
Least Squares Auto-Tuning
Least Squares Auto-Tuning
Shane T. Barratt
Stephen P. Boyd
MoMe
139
26
0
10 Apr 2019
On the Equivalence of Automatic and Symbolic Differentiation
On the Equivalence of Automatic and Symbolic Differentiation
Soeren Laue
107
6
0
05 Apr 2019
Banded Matrix Operators for Gaussian Markov Models in the Automatic
  Differentiation Era
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation EraInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2019
N. Durrande
Vincent Adam
L. Bordeaux
Stefanos Eleftheriadis
J. Hensman
160
26
0
26 Feb 2019
1