Institut
für Grundlagen der Informationsverarbeitung (708)
Lecturer:
Assoc. Prof. Dr. Robert Legenstein
Office hours: by appointment (via e-mail)
E-mail: robert.legenstein@igi.tugraz.at
Homepage: www.igi.tugraz.at/legi/
"To illustrate the utility of learning to learn, it is
worthwhile to compare machine learning to human learning. Humans
encounter a continual stream of learning tasks. They do not just
learn concepts of motor skills, they also learn bias, i.e., they
learn how to generalize. As a result, humans are often able to
generalize correctly from extremely few examples - often just a
single example suffices to teach us a new thing. " [Thrun, S.,
& Pratt, L. (Eds.). Learning to learn. (2012)].
In this seminar, we will discuss novel work on "learning to learn". This area of machine learning deals with the following question: How can one train algorithms such that they acquire the ability to learn?
The seminar continues the discussion of last year's CI Seminar
B, but is designed as a stand alone course, i.e., students are
not expected to have visited the previous seminar. However,
basic knowledge in neural networks is expected (e.g., the
computational inteligence lecture) and basic knowledge in
reinforcement lerning would be beneficial.
Talks should be not longer than 35 minutes, and be clear,
interesting and informative, rather than a reprint of the
material. Select what parts of the material you want to present,
and what not, and then present the selected material well
(including definitions not given in the material: look them up
on the web or if that is not successful, ask the seminar
organizers). Often diagrams or figures are useful for a talk. on
the other hand, giving in the talk numbers of references that
are listed at the end is a no-no (a talk is an online process,
not meant to be read). For the same reasons you can also quickly
repeat earlier definitions or so if you suspect that the
audience may not remember it.
Talks will be assigned at the first seminar meeting on October
3, 15:15-17:00. Students are requested to have a quick
glance at the papers prior to this meeting in order to
determine their preferences. Note that the number of
participants for this seminar will be limited. Preference will
be given to students who
Date | # | Topic / paper title | Presenter 1 | Presenter 2 | Presentation |
21.11.2017 | 1 | Building Machines that learn and think like people | Gabler | Jente | |
21.11.2017 | 2 | LSTM: A search space odyssey | Gigerl | Petschenig | |
28.11.2017 | 3 | REINFORCE + Reinforcement learning | Zincke | Walch | |
5.11.2017 | 4 | Asynchronous methods for deep reinforcement learning | Loidl | Nistelberger | |
5.12.2017 | 5 | Neural architecture search with reinforcement learning | Hasler | Hopfgartner | |
12.12.2017 | 6 | Learning to reinforcement learn | Unterholzner | Miguel Yuste Fernandez Alonso | |
12.12.2017 | 7 | RL2: Fast Reinforcement Learning via Slow Reinforcement Learning | Ahmetovic | Music | |
9.1.2018 | - | The IGI-L2L software framework | Kraisnikovic | Plank | |
9.1.2018 | 11 | Learning to optimize | Moling | Reisinger | canceled |
16.1.2018 | 12 | Meta-SGD: Learning to Learn Quickly for Few Shot Learning | Poier | Basirat | |
16.1.2018 | 13 | One-shot learning with memory-augmented neural networks | Malle | Scherr | |
23.1.2018 | 8 | Motor task variation induces structural learning | Giegold | Weichselbaum |