Analysis-Driven Design of Representations For Sensing-Action Systems

Abstract

We have developed what is, to the best of our knowledge, the first complete theory of representation for decision and control task, which has shown not only to encompass and explain all known phenomenology in deep neural network-based representation learning, but also to predict phenomena that were thus far unexplained.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2017
Accession Number
AD1040878

Entities

People

  • Stefano Soatto

Organizations

  • University of California

Tags

Communities of Interest

  • Autonomy
  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Automata Theory
  • Computer Science
  • Computer Vision
  • Computers
  • Detectors
  • Dimensionality Reduction
  • Electrical Engineering
  • Information Theory
  • Neural Networks
  • Pattern Recognition
  • Signal Processing
  • Students

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks