DiffML: End-to-end Differentiable ML Pipelines-Reference-Cited by-同舟云学术

DiffML: End-to-end Differentiable ML Pipelines

Published:2023-06-18 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Seventh Workshop on Data Management for End-to-End Machine Learning
language:
Short-container-title:

Author:

Hilprecht Benjamin¹^ORCID,Hammacher Christian²^ORCID,Reis Eduardo S³^ORCID,Abdelaal Mohamed²^ORCID,Binnig Carsten¹⁴^ORCID

Affiliation:

1. TU Darmstadt, Darmstadt, DE

2. Software AG, Darmstadt, DE

3. TU Darmstadt, Darmstadt, BR

4. DFKI, Darmstadt, Germany

Funder

BMBF KompAKI

BMBF AICoM

ITEA IML4E

Merck Sustainability Hub (project LC IT Age)

BMBF NHR4CES

HMWK cluster project 3AI

Publisher

ACM

Reference11 articles.

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2. ReStore - Neural Data Completion for Relational Databases

3. Baran

4. Felix Neutatz , Binger Chen , Ziawasch Abedjan , and Eugene Wu. 2021. From Cleaning before ML to Cleaning for ML. Data Engineering ( 2021 ), 24. Felix Neutatz, Binger Chen, Ziawasch Abedjan, and Eugene Wu. 2021. From Cleaning before ML to Cleaning for ML. Data Engineering (2021), 24.

5. ED2: A Case for Active Learning in Error Detection

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