Composable Robust Structured Data Inference

Abstract

Messy data heterogeneous values, missing entries, and large errors presents a major obstacle to automated data-driven discovery of models. Data cleaning is the first step in any data processing pipeline, and has serious consequences for the results of any subsequent analysis. Yet this step is generally performed using ad-hoc methods. This effort seeks to cleanse the data set, and build a structured data interface to reduce noise from data sets, to deliver a production of clean data sets, and leverage model selection and automated techniques.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2021
Accession Number
AD1146477

Entities

People

  • Madeleine Udell

Organizations

  • Cornell University

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  • Autonomy

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  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Artificial Intelligence
  • Big Data
  • California
  • Computer Programming
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  • Data Mining
  • Data Science
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Fields of Study

  • Computer science

Readers

  • Computer Science.
  • Environmental Engineering.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference