Detecting Concept Drift With Neural Network Model Uncertainty

Detecting Concept Drift With Neural Network Model Uncertainty

5 July 2021

Jakob Schöffer

Niklas Kühl

Papers citing "Detecting Concept Drift With Neural Network Model Uncertainty"

17 / 17 papers shown

Title
Detecting Concept Drift in Neural Networks Using Chi-squared Goodness of Fit Testing Jacob Glenn Ayers Buvaneswari A. Ramanan Manzoor A. Khan 27 0 0 07 May 2025
Time to Retrain? Detecting Concept Drifts in Machine Learning Systems Tri Minh Triet Pham Karthikeyan Premkumar Mohamed Naili Jinqiu Yang AI4TS 16 0 0 11 Oct 2024
Concept Drift Detection using Ensemble of Integrally Private Models Ayush K. Varshney V. Torra 23 4 0 07 Jun 2024
Active Test-Time Adaptation: Theoretical Analyses and An Algorithm Shurui Gui Xiner Li Shuiwang Ji TTA 61 11 0 07 Apr 2024
Securing Reliability: A Brief Overview on Enhancing In-Context Learning for Foundation Models Yunpeng Huang Yaonan Gu Jingwei Xu Zhihong Zhu Zhaorun Chen Xiaoxing Ma 35 3 0 27 Feb 2024
An Empirical Study of Uncertainty Estimation Techniques for Detecting Drift in Data Streams Anton Winter Nicolas Jourdan Tristan Wirth Volker Knauthe Arjan Kuijper 9 1 0 22 Nov 2023
Reliability Quantification of Deep Reinforcement Learning-based Control Hitoshi Yoshioka Hirotada Hashimoto 17 0 0 29 Sep 2023
Towards Practicable Sequential Shift Detectors Oliver Cobb A. V. Looveren 21 0 0 27 Jul 2023
On the Connection between Concept Drift and Uncertainty in Industrial Artificial Intelligence J. Lobo I. Laña E. Osaba Javier Del Ser 27 1 0 14 Mar 2023
Active learning for data streams: a survey Davide Cacciarelli M. Kulahci 25 40 0 17 Feb 2023
DetAIL : A Tool to Automatically Detect and Analyze Drift In Language Nishtha Madaan Adithya Manjunatha Hrithik Nambiar Aviral Goel H. Kumar Diptikalyan Saha Srikanta J. Bedathur 12 4 0 03 Nov 2022
Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability I. Laña Ignacio I. Olabarrieta Javier Del Ser 32 1 0 28 Oct 2022
Machine Learning Operations (MLOps): Overview, Definition, and Architecture Dominik Kreuzberger Niklas Kühl Sebastian Hirschl VLM AI4CE 11 330 0 04 May 2022
Autoregressive based Drift Detection Method M. Z. A. Mayaki M. Riveill 15 4 0 09 Mar 2022
On The Reliability Of Machine Learning Applications In Manufacturing Environments Nicolas Jourdan S. Sen E. J. Husom Enrique Garcia-Ceja Tobias Biegel J. Metternich OOD 17 9 0 13 Dec 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 273 5,660 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015