Neural Coffee



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A Deep Dive into Deep Learning

Immerse yourself in the exciting world of Deep Learning at Neural Coffee!

Every Friday, we’ll gather to discuss the latest advancements and ideas in the field and expand our knowledge in a relaxed and friendly environment.

All discussions will be held in English.

Open to All, Welcome to All

Neural Coffee is open to anyone who shares a passion for deep learning, regardless of their background or affiliation.
Whether you’re a student, staff member, or simply have an interest in Deep Learning, this reading group offers a chance to meet and connect with a community of like-minded individuals.
The only requirement is to have basic knowledge of Deep Learning.
If you have completed a deep learning course (at the uni or online) and have programmed any neural network architecture from scratch on Tensorflow, Pytorch, or NumPy you are good to go!
So come and join us, and be a part of the excitement!

Be Prepared, Be Involved

Take charge of your learning experience and bring a paper, video, or article to share and discuss at each meeting.

Engage in lively discussions and ask questions.

With coffee provided to keep you fueled, the only thing missing is you!

When? Where? Who?

Time: Every Friday 11:00 am – 13:00pm

Location: Seminar Room D, Franz Josef Street 18, 8700 Leoben

Organizer: Fotis Lygerakis, Doctoral Student at the Chair of Cyber-Physical Systems


Feel free to address any questions regarding Neural Coffee to

Meetings Agenda

  • 24.02.2023 – Introduction of Neural Coffee Reading Group
  • 03.03.2023 – David Ha, rgen Schmidhuber, World Models, 2018, NIPS [paper] [article]
  • 10.03.2023 – Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, et al, Generative Adversarial Nets, 2014, NIPS [paper] [article]
  • 17.03.2023
  •  24.03.2023 Vaswani, A., Shazeer, et al. (2017). Attention is All you Need. In Advances in Neural Information Processing Systems. Curran Associates, Inc. [paper] [article 1] [article 2] [video 1] [video 2]
  •  31.03.2023 Geoffrey Hinton (2022). The Forward-Forward Algorithm: Some Preliminary Investigations. Presented at Advances in Neural Information Processing Systems 2022. [paper][article 1][article 2][video 1][video 2]
  • 21.04.2023 Li Jing, Pascal Vincent, Yann LeCun, & Yuandong Tian, Understanding Dimensional Collapse in Contrastive Self-supervised Learning. ICLR 2022 [paper] [blog] [video] [code]
  • 12.05.2023 Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, & Armand Joulin (2020). Unsupervised Learning of Visual Features by Contrasting Cluster Assignments. NeurIPS. 2020 [paper][blog]

Member Book Suggestions

From Bacteria to Bach and Back: The Evolution of Minds, by Daniel C. Dennett, ISBN-10: 0393355500