Publication Type

Journal Article

Version

acceptedVersion

Publication Date

10-2022

Abstract

Machine learning's grand ambition is the mathematical modeling of reality. The recent years have seen major advances using deep-learned techniques that model reality implicitly; however, corresponding advances in explicit mathematical models have been noticeably lacking. We believe this dichotomy is rooted in the limitations of the current statistical tools, which struggle to make sense of the high dimensional generative processes that natural data seems to originate from. This paper proposes a new, distance based statistical technique which allows us to develop elegant mathematical models of such generative processes. Our model suggests that each semantic concept has an associated distinctive-shell which encapsulates almost-all instances of itself and excludes almost-all others. creating the first, explicit mathematical representation of the constraints which make machine learning possible.

Keywords

Semantics, Mathematical model, Machine learning, Random variables, Manifolds, Machine learning algorithms, Prediction algorithms

Discipline

Artificial Intelligence and Robotics

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volume

44

Issue

10

First Page

6438

Last Page

6453

ISSN

0162-8828

Identifier

10.1109/TPAMI.2021.3084598

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TPAMI.2021.3084598

Share

COinS