RUB ML Reading Group

The INI ML Reading Group is the machine learning reading group at the INI at RUB.

RUB ML Reading Group

The RUB ML Reading Group is the machine learning reading group at the Ruhr-University Bochum organized by the Institute of Neural Computation. We meet once in two weeks to informally discuss machine learning and deep learning papers that are related to our research, of general interest, or just some paper one of us found cool.

Currently this happens (mostly) on Wednesdays at 16:00 virtually on Zoom.

If you would like to attend the meetings, subscribe to the ML Reading Group mailing list for event announcements.

To update this page, please send a pull request on the Github Repository.

Wishlist

Past meetings

Date Moderator Paper
11.05.2022 Jan Seth, A.K., Bayne, T., 2022. Theories of consciousness. Nature Reviews Neuroscience 1–14. https://doi.org/10.1038/s41583-022-00587-4 (URL)
13.04.2022 Zahra Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M., 2022. Hierarchical Text-Conditional Image Generation with CLIP Latents (No. arXiv:2204.06125). arXiv. https://doi.org/10.48550/arXiv.2204.06125 (URL)
30.03.2022 Moritz Locatello, F., Bauer, S., Lucic, M., Rätsch, G., Gelly, S., Schölkopf, B., Bachem, O., 2019. Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (No. arXiv:1811.12359). arXiv. https://doi.org/10.48550/arXiv.1811.12359 (URL)
16.02.2022 Robin Goyal, A., Lamb, A., Hoffmann, J., Sodhani, S., Levine, S., Bengio, Y., Schölkopf, B., 2020. Recurrent Independent Mechanisms, in: International Conference on Learning Representations. (URL)
02.02.2022 David Zimmermann, H., Wu, H., Esmaeili, B., van de Meent, J.-W., 2021. Nested Variational Inference. Advances in Neural Information Processing Systems 34. (URL)
  Moritz Girin, L., Leglaive, S., Bie, X., Diard, J., Hueber, T., Alameda-Pineda, X., 2020. Dynamical variational autoencoders: A comprehensive review. arXiv preprint arXiv:2008.12595. (URL)
21.01.2022 Anand Frankle, J., Carbin, M., 2018. The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks, in: International Conference on Learning Representations. (URL)
  Jan Malach, E., Shalev-Shwartz, S., 2020. The implications of local correlation on learning some deep functions. Advances in Neural Information Processing Systems 33, 1322–1332. (URL) (talk video)
17.12.2022 David Bronstein, M.M., Bruna, J., Cohen, T., Veličković, P., 2021. Geometric deep learning: Grids, groups, graphs, geodesics, and gauges. arXiv preprint arXiv:2104.13478. (URL)
    Keller, T., Welling, M., 2021. Topographic VAEs learn equivariant capsules. Advances in Neural Information Processing Systems 34. (URL)
  Jan Decelle, A., Furtlehner, C., Seoane, B., 2021. Equilibrium and non-equilibrium regimes in the learning of restricted Boltzmann machines. Advances in Neural Information Processing Systems 34. (URL)