dblp: IoT Streams/ITEM@PKDD/ECML 2020 (original) (raw)

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IoT Streams/ITEM@PKDD/ECML 2020: Ghent, Belgium

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  • IoT Streams 2020: Stream Learning
  • IoT Streams 2020: Feature Learning
  • IoT Streams 2020: Unsupervised Machine Learning
  • ITEM 2020: Hardware
  • ITEM 2020: Methods
  • ITEM 2020: Quantization

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    João Gama, Sepideh Pashami, Albert Bifet, Moamar Sayed-Mouchaweh, Holger Fröning, Franz Pernkopf, Gregor Schiele, Michaela Blott:
    IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning - Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers. Communications in Computer and Information Science 1325, Springer 2020, ISBN 978-3-030-66769-6

IoT Streams 2020: Stream Learning

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    Bruno Veloso, João Gama:
    Self Hyper-parameter Tuning for Stream Classification Algorithms. 3-13
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    Minh-Huong Le Nguyen, Fabien Turgis, Pierre-Emmanuel Fayemi, Albert Bifet:
    Challenges of Stream Learning for Predictive Maintenance in the Railway Sector. 14-29
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    Hadi Fanaee-T, Mohamed-Rafik Bouguelia, Mahmoud Rahat, Jonathan Blixt, Harpal Singh:
    CycleFootprint: A Fully Automated Method for Extracting Operation Cycles from Historical Raw Data of Multiple Sensors. 30-44
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    Muhammad Atif Qureshi, Luis Miralles-Pechuán, Jason Payne, Ronan O'Malley, Brian Mac Namee:
    Valve Health Identification Using Sensors and Machine Learning Methods. 45-60
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    Mariana Barros, Bruno Veloso, Pedro Mota Pereira, Rita P. Ribeiro, João Gama:
    Failure Detection of an Air Production Unit in Operational Context. 61-74

IoT Streams 2020: Feature Learning

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    Patrick Klein, Niklas Weingarz, Ralph Bergmann:
    Enhancing Siamese Neural Networks Through Expert Knowledge for Predictive Maintenance. 77-92
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    Cheolhwan Oh, Junhyung Moon, Jongpil Jeong:
    Explainable Process Monitoring Based on Class Activation Map: Garbage In, Garbage Out. 93-105
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    Tanja Tornede, Alexander Tornede, Marcel Wever, Felix Mohr, Eyke Hüllermeier:
    AutoML for Predictive Maintenance: One Tool to RUL Them All. 106-118
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    Kunru Chen, Sepideh Pashami, Slawomir Nowaczyk, Emilia Johansson, Gustav Sternelöv, Thorsteinn S. Rögnvaldsson:
    Forklift Truck Activity Recognition from CAN Data. 119-126
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    Vandan Revanur, Ayodeji Ayibiowu, Mahmoud Rahat, Reza Khoshkangini:
    Embeddings Based Parallel Stacked Autoencoder Approach for Dimensionality Reduction and Predictive Maintenance of Vehicles. 127-141

IoT Streams 2020: Unsupervised Machine Learning

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    Yonas Y. Tefera, Maarten Meire, Stijn Luca, Peter Karsmakers:
    Unsupervised Machine Learning Methods to Estimate a Health Indicator for Condition Monitoring Using Acoustic and Vibration Signals: A Comparison Based on a Toy Data Set from a Coffee Vending Machine. 145-159
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    Pieter Bonte, Sander Vanden Hautte, Annelies Lejon, Veerle Ledoux, Filip De Turck, Sofie Van Hoecke, Femke Ongenae:
    Unsupervised Anomaly Detection for Communication Networks: An Autoencoder Approach. 160-172
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    Lingyun Cheng, Sadhana Sundaresh, Mohamed-Rafik Bouguelia, Onur Dikmen:
    Interactive Anomaly Detection Based on Clustering and Online Mirror Descent. 173-186

ITEM 2020: Hardware

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    Philipp Spilger, Eric Müller, Arne Emmel, Aron Leibfried, Christian Mauch, Christian Pehle, Johannes Weis, Oliver Breitwieser, Sebastian Billaudelle, Sebastian Schmitt, Timo C. Wunderlich, Yannik Stradmann, Johannes Schemmel:
    hxtorch: PyTorch for BrainScaleS-2 - Perceptrons on Analog Neuromorphic Hardware. 189-200
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    Johannes Weis, Philipp Spilger, Sebastian Billaudelle, Yannik Stradmann, Arne Emmel, Eric Müller, Oliver Breitwieser, Andreas Grübl, Joscha Ilmberger, Vitali Karasenko, Mitja Kleider, Christian Mauch, Korbinian Schreiber, Johannes Schemmel:
    Inference with Artificial Neural Networks on Analog Neuromorphic Hardware. 201-212
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    Dennis Rieber, Holger Fröning:
    Search Space Complexity of Iteration Domain Based Instruction Embedding for Deep Learning Accelerators. 213-228
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    Kevin Stehle, Günther Schindler, Holger Fröning:
    On the Difficulty of Designing Processor Arrays for Deep Neural Networks. 229-240

ITEM 2020: Methods

ITEM 2020: Quantization

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