Nonsmooth differential calculus for robustness in Machine Learning

Abstract

Deep learning and AI s rely on the use of automatic differentiation which can be considered as a clever way to orchestrate elementary learning mechanisms. In mathematical terms the recent theory of conservative gradients allowed to rigorously model autodiff algorithms as implemented on modern machine learning computer libraries. The goal of this proposal is to deepen this theory further to derive sensitivity and robustness tools for deep learning.

Document Details

Document Type
DoD Grant Award
Publication Date
Apr 20, 2023
Source ID
FA86552217012

Entities

People

  • Jerome Bolte

Organizations

  • Air Force Office of Scientific Research
  • United States Air Force

Tags

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Calculus or Mathematical Analysis

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks