An Algebra of Machine Learners with Applications

Rao, Nageswara

doi:10.23919/FUSION49465.2021.9626918

An Algebra of Machine Learners with Applications

Conference · 01 November 2021

DOI:https://doi.org/10.23919/FUSION49465.2021.9626918· OSTI ID:1883987

^[1]

ORNL

Machine learning (ML) methods are increasingly being applied to solve complex, data-driven problems in diverse areas, by exploiting the physical laws derived from first principles such as thermal hydraulics and the abstract laws developed recently for data and computing infrastructures. These physical and abstract laws encapsulate, typically in compact algebraic forms, the critical knowledge that complements data-driven ML models. We present a unified perspective of these laws and ML methods using an abstract algebra (A;⊕,Ⓧ), wherein the performance estimation and classification tasks are characterized by the additive ⊕ operations, and the diagnosis, reconstruction, and optimization tasks are characterized by the difference Ⓧ operations. This abstraction provides ML codes and their performance characterizations that are transferable across different areas. We describe practical applications of these abstract operations using examples of throughput profile estimation tasks in data transport infrastructures, and power-level and sensor error estimation tasks in nuclear reactor systems.

Research Organization:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

DOE Contract Number:: AC05-00OR22725

OSTI ID:: 1883987

Resource Relation:: Conference: International Conference on Information Fusion - Sun City, , South Africa - 11/1/2021 4:00:00 AM-11/4/2021 4:00:00 AM

Country of Publication:: United States

Language:: English

References (12)

Experiments and Analyses of Data Transfers over Wide-Area Dedicated Connections No authors listed 2017 26th International Conference on Computer Communication and Networks (ICCCN) https://doi.org/10.1109/ICCCN.2017.8038432		July 2017
TCP Throughput Profiles Using Measurements over Dedicated Connections No authors listed Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing - HPDC '17 https://doi.org/10.1145/3078597.3078615		January 2017
Estimation of sensor measurement errors in reactor coolant systems using multi-sensor fusion No authors listed Nuclear Engineering and Design https://doi.org/10.1016/j.nucengdes.2020.111024		April 2021
Information fusion methods based on physical laws No authors listed IEEE Transactions on Pattern Analysis and Machine Intelligence https://doi.org/10.1109/TPAMI.2005.12		January 2005
Fusion method for physical systems based on physical laws No authors listed Proceedings of the Third International Conference on Information Fusion https://doi.org/10.1109/IFIC.2000.862456		January 2000
An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures No authors listed Proceedings of the Machine Learning on HPC Environments https://doi.org/10.1145/3146347.3146356		November 2017
Information fusion method for system identification based on sensitivity analysis No authors listed Proceedings of the Third International Conference on Information Fusion https://doi.org/10.1109/IFIC.2000.862463		January 2000
Training a 3-node neural network is NP-complete No authors listed Neural Networks https://doi.org/10.1016/S0893-6080(05)80010-3		January 1992
Computational aspects of computer viruses No authors listed Computers & Security https://doi.org/10.1016/0167-4048(89)90089-8		June 1989
The inexpressibility of validity No authors listed Analysis https://doi.org/10.1093/analys/ant096		January 2014
On fusers that perform better than best sensor No authors listed IEEE Transactions on Pattern Analysis and Machine Intelligence https://doi.org/10.1109/34.946993		January 2001
Reactor Power Level Estimation by Fusing Multi-Modal Sensor Measurements No authors listed 2020 IEEE 23rd International Conference on Information Fusion (FUSION) https://doi.org/10.23919/FUSION45008.2020.9190219		July 2020

Similar Records

Generalization Equations for Machine Learners Based on Physical and Abstract Laws

Conference · 2021 · OSTI ID:1823318

Rao, Nageswara

A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids

Journal Article · 2022 · Scientific Data · OSTI ID:1978710

Zheng, Xiangtian; Xu, Nan; Trinh, Loc; +5 more

Machine Learning Algorithms for Matching Theories, Simulations, and Observations in Cosmology (Final Project)

Technical Report · 2018 · OSTI ID:1572709

Poczos, Barnabas

An Algebra of Machine Learners with Applications

Citation Formats

References (12)

Similar Records

Related Subjects