𝜆ₛ: computable semantics for differentiable programming with higher-order functions and datatypes

Abstract

Deep learning is moving towards increasingly sophisticated optimization objectives that employ higher-order functions, such as integration, continuous optimization, and root-finding. Since differentiable programming frameworks such as PyTorch and TensorFlow do not have first-class representations of these functions, developers must reason about the semantics of such objectives and manually translate them to differentiable code.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 04, 2021
Source ID
10.1145/3434284

Entities

People

  • Benjamin Sherman
  • Jesse Michel
  • Michael Carbin

Organizations

  • Massachusetts Institute of Technology
  • Office of Naval Research

Tags

Fields of Study

  • Computer science

Readers

  • Approximation Theory.
  • Neural Network Machine Learning.
  • Software Engineering.

Technology Areas

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