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Symbolic Deep Networks: A Psychologically Inspired Lightweight and Efficient Approach to Deep Learning.

Vladislav D Veksler1,2, Blaine E Hoffman2, Norbou Buchler2

  • 1DCS Corp, Alexandria, VA.

Topics in Cognitive Science
|October 5, 2021
PubMed
Summary
This summary is machine-generated.

Symbolic deep networks (SDNs) show comparable accuracy to deep neural networks (DNNs) but are more efficient and robust. These cognition-inspired models offer a promising direction for human-level AI learning.

Keywords:
Artificial intelligenceCategorizationCognitive architecturesDeep learningMachine learningSupervised learningSymbolic deep learningXAI

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Cognitive Science

Background:

  • Deep neural networks (DNNs) have driven recent AI/ML successes.
  • Deep hierarchical memory networks, inspired by cognitive science, offer an alternative.
  • DNNs face challenges in learning efficiency, catastrophic interference, and explainability.

Purpose of the Study:

  • To evaluate cognition-inspired symbolic deep networks (SDNs) against DNNs.
  • To assess SDN performance in classification accuracy, efficiency, and robustness.
  • To explore the potential of SDNs for human-level category learning.

Main Methods:

  • Comparative analysis of SDNs and DNNs on popular ML datasets.
  • Simulations to measure classification accuracy, learning efficiency, and robustness.
  • Evaluation of resistance to catastrophic interference and noisy attributes.

Main Results:

  • SDNs achieve accuracy comparable to DNNs across various datasets.
  • SDNs demonstrate significantly higher learning efficiency than DNNs.
  • SDNs exhibit robustness to irrelevant data attributes and catastrophic interference.

Conclusions:

  • SDNs present a viable path toward human-level accuracy and efficiency in AI.
  • Cognitively inspired approaches can enhance machine learning frameworks.
  • SDNs offer advantages in learning efficiency and memory robustness over traditional DNNs.