Distribution and Histogram (DisH) Learning

Abstract

Machine learning has made incredible advances in the last couple of decades. Notwithstanding, a lot of this progress has been limited to basic point-estimation tasks. That is, a large bulk of attention has been geared at solving problems that take in a static finite vector and map it to another static finite vector. However, we do not navigate through life in a series of point-estimation problems, mapping x to y. Instead, we find broad patterns and gather a far-sighted understanding of data by considering collections of points like sets, sequences, and distributions. Thus, contrary to what various billionaires, celebrity theoretical physicists, and sci-fi classics would lead you to believe, true machine intelligence is fairly out of reach currently. In order to bridge this gap, this thesis develops algorithms that understand data at an aggregate, holistic level.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2018
Accession Number
AD1168003

Entities

People

  • Junier Oliva

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Change Detection
  • Cognitive Science
  • Computational Fluid Dynamics
  • Computational Science
  • Computer Languages
  • Computer Vision
  • Data Mining
  • Information Processing
  • Information Retrieval
  • Information Science
  • Kernel Functions
  • Machine Learning
  • Monte Carlo Method
  • Network Science
  • Neural Networks

Readers

  • Military History of the United States in the 20th Century.
  • Neural Network Machine Learning.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Learning Algorithms