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A Theoretical Framework for AI-driven data quality monitoring in
  high-volume data environments

A Theoretical Framework for AI-driven data quality monitoring in high-volume data environments

11 October 2024
Nikhil Bangad
Vivekananda Jayaram
Manjunatha Sughaturu Krishnappa
Amey Ram Banarse
Darshan Mohan Bidkar
Akshay Nagpal
Vidyasagar Parlapalli
ArXiv (abs)PDFHTML

Papers citing "A Theoretical Framework for AI-driven data quality monitoring in high-volume data environments"

12 / 12 papers shown
Title
Attention is not not Explanation
Attention is not not Explanation
Sarah Wiegreffe
Yuval Pinter
XAIAAMLFAtt
124
914
0
13 Aug 2019
GAIN: Missing Data Imputation using Generative Adversarial Nets
GAIN: Missing Data Imputation using Generative Adversarial Nets
Jinsung Yoon
James Jordon
M. Schaar
GAN
68
1,029
0
07 Jun 2018
Deep Visual Domain Adaptation: A Survey
Deep Visual Domain Adaptation: A Survey
Mei Wang
Weihong Deng
OOD
102
2,020
0
10 Feb 2018
Online Learning: A Comprehensive Survey
Online Learning: A Comprehensive Survey
Guosheng Lin
Doyen Sahoo
Jing Lu
P. Zhao
OffRL
103
649
0
08 Feb 2018
NIMA: Neural Image Assessment
NIMA: Neural Image Assessment
Hossein Talebi
P. Milanfar
3DH
92
910
0
15 Sep 2017
An Overview of Multi-Task Learning in Deep Neural Networks
An Overview of Multi-Task Learning in Deep Neural Networks
Sebastian Ruder
CVBM
161
2,833
0
15 Jun 2017
A Unified Approach to Interpreting Model Predictions
A Unified Approach to Interpreting Model Predictions
Scott M. Lundberg
Su-In Lee
FAtt
1.1K
22,090
0
22 May 2017
Federated Learning: Strategies for Improving Communication Efficiency
Federated Learning: Strategies for Improving Communication Efficiency
Jakub Konecný
H. B. McMahan
Felix X. Yu
Peter Richtárik
A. Suresh
Dave Bacon
FedML
314
4,659
0
18 Oct 2016
Communication-Efficient Learning of Deep Networks from Decentralized
  Data
Communication-Efficient Learning of Deep Networks from Decentralized Data
H. B. McMahan
Eider Moore
Daniel Ramage
S. Hampson
Blaise Agüera y Arcas
FedML
412
17,615
0
17 Feb 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAttFaML
1.2K
17,071
0
16 Feb 2016
Distributed Representations of Words and Phrases and their
  Compositionality
Distributed Representations of Words and Phrases and their Compositionality
Tomas Mikolov
Ilya Sutskever
Kai Chen
G. Corrado
J. Dean
NAIOCL
406
33,573
0
16 Oct 2013
Practical Bayesian Optimization of Machine Learning Algorithms
Practical Bayesian Optimization of Machine Learning Algorithms
Jasper Snoek
Hugo Larochelle
Ryan P. Adams
382
7,981
0
13 Jun 2012
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