• Genome Data Science

    We develop methods and tools to work with tens of thousands of genomes and analyze and integrate the corresponding data.

    Model of DNA double helix in front of a student.
    © Universität Bielefeld

Recent Advances in Deep Learning


392133 Schönhuth Summer 2020 Thu 12:15-13:45 (S) in ZOOM

Contents

In this seminar we will discuss the most recent advances and successes of Deep Learning. Therefore, we will provide a brief introduction into Deep Learning itself and then further discuss the most recent techniques and methods presented by the leading research groups of the field. Each participant will review a corresponding publication and summarize its contents in a 45 minute presentation. As a guideline see the book “Dive into Deep Learning” (https://d2l.ai/), Chapters 7 and 17.

The students are supposed to form groups of three, which probably leads to 5-6 groups. (a total of 20 participants are registered) Each group should work on a scientific article and present and discuss it within a maximum of 90 minutes.

Groups

  1. Nina, Francesco, Ivan (BERT; June 18)
  2. Jan-Hendrik, Florian, Alexander (GANs; June 25)
  3. Mumtaz, Eren, Muhammad (GNN; July 02)

Literature

  1. Bidirectional Encoder Representations from Transformers (BERT)
  2. Generative Adversarial Networks (GAN's)
  3. Graph Neural Networks (GNN)

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