One-shot ontogenetic learning in biomedical datastreams

John Kalantari, Michael A. Mackey

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Recent technological advances in the biological and physical sciences have allowed for the generation of large quantity datasets necessary for applying deep neural networks. Despite the demonstrable success of these methods in a variety of tasks including image classification, machine translation, and query-answering, among others, their widespread adoption in biomedical research has been tempered due to issues inherent to modeling complex biological systems not readily addressed by traditional gradient-based neural networks. We consider the problem of unsupervised, general-purpose learning in biological sequence data, wherein variable-order temporal dependencies, multi-dimensionality and uncertainty in model structure and data are the norm. To successfully model and learn these dependencies in an intuitive and holistic manner, we have utilized the data abstraction of Simplicial Grammar within a Bayesian learning framework. We demonstrate that this framework offers the ability to quickly encode and integrate new information, and perform prediction tasks without extensive, iterative training.

Original languageEnglish (US)
Title of host publicationArtificial General Intelligence - 10th International Conference, AGI 2017, Proceedings
EditorsTom Everitt, Ben Goertzel, Alexey Potapov
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783319637020
StatePublished - 2017
Event10th International Conference on Artificial General Intelligence, AGI 2017 - Melbourne, Australia
Duration: Aug 15 2017Aug 18 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10414 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th International Conference on Artificial General Intelligence, AGI 2017


  • Artificial intelligence
  • Bayesian nonparametrics
  • Probabilistic generative models
  • Simplicial complexes
  • Systems biology
  • Unsupervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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