CAIN 2023
Mon 15 - Sat 20 May 2023 Melbourne, Australia
co-located with ICSE 2023
Mon 15 May 2023 19:00 - 19:20 at Virtual - Zoom for CAIN - Training & Learning Chair(s): Rrezarta Krasniqi

The Industrial Internet of Things (IIoT) leverages thousands of interconnected sensors and computing devices to monitor and control large and complex industrial processes. Machine learning (ML) applications in IIoT use data acquired from multiple sensors to perform tasks such as predictive maintenance. While remembering useful learning from the past, these applications need to adapt learning for evolving sensor data stemming from changes in industrial processes and environmental conditions. This paper presents a continual learning pipeline to learn from the evolving data while replaying selected parts of the old data. The pipeline is configured to produce ML experiences (e.g., training a baseline neural network model), improve the baseline model with the new data while replaying part of the old data, and infer/predict using a specific model version given a stream of IIoT sensor data. We have evaluated our approach from an AI Engineering perspective using three industrial case studies, i.e., predicting tool wear, remaining useful lifetime and anomalies from sensor data acquired from CNC machining and broaching operations. Our results show that configuring experiences for replay-driven continual learning allows dynamic maintenance of ML performance on evolving data while minimizing the excessive accumulation of legacy sensor data.

Mon 15 May

Displayed time zone: Hobart change

19:00 - 20:30
Training & LearningPapers at Virtual - Zoom for CAIN
Chair(s): Rrezarta Krasniqi University of North Carolina at Charlotte

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19:00
20m
Long-paper
Replay-Driven Continual Learning for the Industrial Internet of Things
Papers
Sagar Sen , Simon Myklebust Nielsen University of Oslo, Norway, Erik Johannes Husom SINTEF Digital, Arda Goknil SINTEF Digital, Simeon Tverdal SINTEF Digital, Leonardo Sastoque Pinilla Centro de Fabricación Avanzada Aeronáutica (CFAA)
19:20
20m
Long-paper
Towards Understanding Model Quantization for Reliable Deep Neural Network Deployment
Papers
Qiang Hu University of Luxembourg, Yuejun GUo University of Luxembourg, Maxime Cordy University of Luxembourg, Luxembourg, Xiaofei Xie Singapore Management University, Wei Ma Nanyang Technological University, Singapore, Mike Papadakis University of Luxembourg, Luxembourg, Yves Le Traon University of Luxembourg, Luxembourg
19:40
20m
Long-paper
Exploring Hyperparameter Usage and Tuning in Machine Learning ResearchDistinguished paper Award Candidate
Papers
Sebastian Simon Leipzig University, Nikolay Kolyada , Christopher Akiki Leipzig University, Martin Potthast Leipzig University, Benno Stein Bauhaus-University Weimar, Norbert Siegmund Leipzig University
Pre-print
20:00
15m
Short-paper
An Initial Analysis of Repair and Side-effect Prediction for Neural Networks
Papers
Yuta Ishimoto Kyushu University, Ken Matsui Kyushu University, Masanari Kondo Kyushu University, Naoyasu Ubayashi Kyushu University, Yasutaka Kamei Kyushu University
Pre-print