PhD Candidate Machine Learning

Sindy Löwe

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About me

I am a first year PhD student in Machine Learning at the University of Amsterdam, supervised by Prof. Max Welling. I am interested in self-supervised representation learning, focussing on mutual information maximization approaches and on structured representations.

Before finding my way into Machine Learning, I completed a BSc in Cognitive Science at the University of Tübingen. During this study, I worked at the Max Planck Institute for Biological Cybernetics conducting research on the representation of visual percepts in the neuronal activity of rodents. After completing a BSc in Informatics at the University of Tübingen, I graduated with a MSc in Artificial Intelligence at the University of Amsterdam.

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Sindy Löwe, Sindy Lowe, Sindy Loewe

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Publications

  • NeurIPS 2019

    Putting An End to End-to-End: Gradient-Isolated Learning of Representations

    Honorable Mention for Outstanding New Directions Paper Award

    Sindy Löwe*, Peter O'Connor, Bastiaan S. Veeling*

    We show that we can train a neural network without end-to-end backpropagation and achieve competitive performance.
    [Link, PDF(arXiv), Code]

    An earlier version of this paper was accepted at the ICML Workshop for Self-Supervised Learning (2019).
    [PDF, Link to Workshop Site]

  • Master's Thesis (2019)

    Greedy InfoMax for Self-Supervised Representation Learning

    Sindy Löwe

    Resulted in publication:
    "Putting An End to End-to-End: Gradient-Isolated Learning of Representations"
    [PDF]

  • VISAPP 2019

    Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

    Paul Bergmann, Sindy Löwe, Michael Fauser, David Sattlegger, Carsten Steger

    Illustrating the shortcomings of pixel-wise reconstruction errors when using autoencoders for unsupervised defect segmentation on image data and proposing to use a perceptual loss based on structural similarity.
    [Link, PDF(arXiv)]

  • Extended Abstract at Neuroscience 2015

    Temporal predictability of visual target onset by audition leads to decrease in evoked neural activity in mouse V1

    Sindy Löwe, Masataka Watanabe, Nikos Logothetis, Laura Busse, Steffen Katzner

    Investigating how temporal predictability affects the processing of a visual stimulus by measuring the responses of single neurons in the mouse primary visual cortex (V1).
    [Link]

Contact

Sindy Löwe

LOEWE [dot] SINDY [at] GMAIL [dot] com

AMLab, University of Amsterdam

Science Park 904, 1098 XH Amsterdam, The Netherlands

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