Discovery of Deep Structure from Unlabeled Data

Abstract

This research project addressed the problem of learning useful "deep" representations from unlabeled data. The major goal was to innovate new unsupervised deep learning algorithms capable of learning important semantic structure in the input data in a domain general way. At the conclusion of this project, these goals stand fulfilled. The lab produced a variety of new and influential learning algorithms including Independent Subspace Analysis (ISA); Reconstruction Independent Components Analysis (RICA); recursive neural networks; and recursive tensor networks, among others. These algorithms have posted state-of-the-art results across a number of domains and tasks, and have had impact on both academia and industry.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2014
Accession Number
ADA614158

Entities

People

  • Andrew Y. Ng
  • Christopher D. Manning

Organizations

  • Stanford University

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence Software
  • Automated Speech Recognition
  • Bayesian Networks
  • Computer Languages
  • Computer Vision
  • Convolutional Neural Networks
  • Deep Learning
  • Dimensionality Reduction
  • Feature Extraction
  • Generative Models
  • Image Recognition
  • Machine Learning
  • Natural Language Processing
  • Neural Networks
  • Object Recognition
  • Probabilistic Models

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Research Science/Academic Research
  • Strategic Security Studies

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks