1st International Workshop on Industrial Machine Learning Call for Papers

1st International Workshop on Industrial Machine Learning
in conjunction with ICPR 2020
January 11, 2020 - Milan, Italy

Website: https://sites.google.com/view/iml2020

Submission deadline: September 25th, 2020
Submission server: https://cmt3.research.microsoft.com/IWIML2020

With the advent of Industry 4.0 paradigm, data has become a valuable
resource, and very often an asset, for every manufacturer. Data from
the market, from machines, from warehouses and many other sources are
now cheaper than ever to be collected and stored. It has been
estimated that in 2020 we will have more than 50B devices connected to
the Industrial Internet of Things, generating more than 500ZB of
data. With such an amount of data, classical data analysis approaches
are not useful and only automated learning methods can be applied to
produce value, a market estimated in more than 200B$
worldwide. Through the use of machine learning techniques
manufacturers can use data to significantly impact their bottom line
by greatly improving production efficiency, product quality, and
employee safety.

The introduction of ML in industry has many benefits that can result
in advantages well beyond efficiency improvements, opening doors to
new opportunities for both practitioners and researchers. Some direct
applications of ML in manufacturing include predictive maintenance,
supply chain management, logistics, quality control, human-robot
interaction, process monitoring, anomaly detection and root cause
analysis to name a few.

This workshop will draw attention to the importance of integrating ML
technologies and ML-based solutions into the manufacturing domain,
while addressing the challenges and barriers to meet the specific
needs of this sector. Workshop participants will have the chance to
- needs and barriers for ML in manufacturing
- state-of-the-art in ML applications to manufacturing
- future research opportunities in this domain

This is an open call for papers, soliciting original contributions
considering recent findings in theory, methodologies, and applications
in the field of industrial machine learning. Position papers
presenting industrial use cases and discussing potential solutions are
welcome. Potential topics include, but are not limited to:

    Robustness-oriented learning algorithms
    Machine learning for robotics (e.g. learning from demonstration)
    Continuous and life-long learning for industrial applications
    Transfer learning and domain adaptation
    Anomaly detection and process monitoring
    ML applications to Predictive Maintenance
    ML applications to Supply Chain and Logistics
    ML applications to Quality Control
    ML for flexible manufacturing
    Deep Learning for industrial applications
    Learning from Big-Data
    Inference in real-time applications
    Machine Learning on Embedded and Edge computing hardware

All the contributions are expected to expose applications to the
industrial sector, possibly with real world case studies. Position
papers presenting new industrial systems and case studies, possibly
reporting preliminary validation studies, are also encouraged.

Papers will be limited to 6 pages according to ICPR format (c.f. Main
conference authors guidelines). All papers will be reviewed by at
least two reviewers with double blind policy. Papers will be selected
based on relevance, significance and novelty of results, technical
merit, and clarity of presentation. Papers will be published in ICPR
All the papers must be submitted using CMT server: 


    Full Paper Submission: September 25, 2020
    Notification of Acceptance: November 10, 2020
    Camera-Ready Paper Due : November 15, 2020

In case of rejection from ICPR main conference, authors can submit
their work to the IML workshop by October 10, 2020. Authors should
address all ICPR reviewers' comments in the submitted paper and submit
the ICPR reviews as supplementary material.

Luigi Di Stefano (University of Bologna, Italy)
Massimiliano Mancini (la Sapienza University, Italy)
Vittorio Murino (University of Verona, Italy)
Paolo Rota (University of Trento, Italy)
Francesco Setti (University of Verona, Italy)

For any inquiry regarding the workshop please contact 
Francesco Setti at francesco.setti@univr.it