ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2006.02080
  4. Cited By
A mathematical model for automatic differentiation in machine learning

A mathematical model for automatic differentiation in machine learning

3 June 2020
Jérôme Bolte
Edouard Pauwels
ArXivPDFHTML

Papers citing "A mathematical model for automatic differentiation in machine learning"

14 / 14 papers shown
Title
Developing Lagrangian-based Methods for Nonsmooth Nonconvex Optimization
Developing Lagrangian-based Methods for Nonsmooth Nonconvex Optimization
Nachuan Xiao
Kuang-Yu Ding
Xiaoyin Hu
Kim-Chuan Toh
29
2
0
15 Apr 2024
Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex functions
Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex functions
Siyuan Zhang
Nachuan Xiao
Xin Liu
61
1
0
18 Mar 2024
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 Parameters
Wonyeol Lee
Sejun Park
A. Aiken
PINN
18
6
0
31 Jan 2023
Differentiating Nonsmooth Solutions to Parametric Monotone Inclusion
  Problems
Differentiating Nonsmooth Solutions to Parametric Monotone Inclusion Problems
Jérôme Bolte
Edouard Pauwels
Antonio Silveti-Falls
26
13
0
15 Dec 2022
Variants of SGD for Lipschitz Continuous Loss Functions in Low-Precision
  Environments
Variants of SGD for Lipschitz Continuous Loss Functions in Low-Precision Environments
Michael R. Metel
30
1
0
09 Nov 2022
Fixed-Point Automatic Differentiation of Forward--Backward Splitting
  Algorithms for Partly Smooth Functions
Fixed-Point Automatic Differentiation of Forward--Backward Splitting Algorithms for Partly Smooth Functions
Sheheryar Mehmood
Peter Ochs
33
3
0
05 Aug 2022
Flexible Differentiable Optimization via Model Transformations
Flexible Differentiable Optimization via Model Transformations
Mathieu Besançon
J. Garcia
B. Legat
Akshay Sharma
18
9
0
10 Jun 2022
Automatic differentiation of nonsmooth iterative algorithms
Automatic differentiation of nonsmooth iterative algorithms
Jérôme Bolte
Edouard Pauwels
Samuel Vaiter
23
22
0
31 May 2022
A Gradient Sampling Algorithm for Stratified Maps with Applications to
  Topological Data Analysis
A Gradient Sampling Algorithm for Stratified Maps with Applications to Topological Data Analysis
Jacob Leygonie
Mathieu Carrière
Théo Lacombe
S. Oudot
13
9
0
01 Sep 2021
The structure of conservative gradient fields
The structure of conservative gradient fields
A. Lewis
Tonghua Tian
AI4CE
19
8
0
03 Jan 2021
Differentiable Programming à la Moreau
Differentiable Programming à la Moreau
Vincent Roulet
Zaïd Harchaoui
15
5
0
31 Dec 2020
Incremental Without Replacement Sampling in Nonconvex Optimization
Incremental Without Replacement Sampling in Nonconvex Optimization
Edouard Pauwels
38
5
0
15 Jul 2020
On Correctness of Automatic Differentiation for Non-Differentiable
  Functions
On Correctness of Automatic Differentiation for Non-Differentiable Functions
Wonyeol Lee
Hangyeol Yu
Xavier Rival
Hongseok Yang
23
40
0
12 Jun 2020
An Inertial Newton Algorithm for Deep Learning
An Inertial Newton Algorithm for Deep Learning
Camille Castera
Jérôme Bolte
Cédric Févotte
Edouard Pauwels
PINN
ODL
20
62
0
29 May 2019
1