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People: Mofassir ul Islam Arif, M.Sc.
Office hour:
C206 Spl, upon request (online only until further notice)
Phone, Fax, Email:
Phone: +49 5121/ 883-40394
e-mail:
Postal address:
Information Systems and Machine Learning Lab
Universitätsplatz 1
University of Hildesheim
31141 Hildesheim
Germany
Visitor address:
Information Systems and Machine Learning Lab
Samelsonplatz 1
University of Hildesheim
31141 Hildesheim
Germany

Publications:

  • Shayan Jawed, Mofassir ul Islam Arif, Ahmed Rashed, Kiran Madhusudhanan, Shereen Elsayed, Mohsan Jameel, Alexei Volk, Andre Hintsches, Marlies Kornfeld, Katrin Lange, Lars Schmidt-Thieme (2022):
    AI and Data-Driven Mobility at Volkswagen Financial Services AG, in arXiv. PDF
  • Yongho Kim, Hanna Lukashonak, Paweena Tarepakdee, Klavdia Zavalich, Mofassir ul Islam Arif (2021):
    Disturbing Target Values for Neural Network Regularization , in arXiv. PDF
  • Mofassir ul Islam Arif, Mohsan Jameel, Josif Grabocka , Lars Schmidt-Thieme (2020):
    Phantom Embeddings: Using Embeddings Space for Model Regularization in Deep Neural Networks , in LWDA.
  • Mohsan Jameel, Mofassir ul Islam Arif, Andre Hintsches, Lars Schmidt-Thieme (2020):
    Automation of Leasing Vehicle Return Assessment Using Deep Learning Models, in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2020). PDF
  • Mofassir ul Islam Arif, Mohsan Jameel, Lars Schmidt-Thieme (2019):
    Directly Optimizing IoU for Bounding Box Localization, in 5th Asian Conference on Pattern Recognition. PDF
  • Mohsan Jameel, Josif Grabocka, Mofassir ul Islam Arif, Lars Schmidt-Thieme (2019):
    Ring-Star : A Sparse Topology for Faster ModelAveraging in Decentralized Parallel SGD , in In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (DMLE@ECML-PKDD 2019). PDF
  • Mofassir ul Islam Arif, Mauricio Camargo , Jan Forkel, Guilherme Holdack, Rafael Drumond, Nicolas Schilling, Tilman Hensch, Ulrich Hegerl, Lars Schmidt-Thieme (2018):
    Depression Diagnosis using Deep Convolutional Neural Networks , in Archives of Data Science, Series A.