Univ.-Prof. Dr. Elmar Rueckert is organizing this research seminar. Topics include research in AI, machine and deep learning, robotics, cyber-physical-systems and process informatics.
Are you an undergraduate, graduate, or doctoral student and want to learn more about AI?
This course will give you the opportunity to listen to research presentations of latest achievements. The target audience are non-experts. Thus no prior knowledge in AI is required.
To get the ECTS credits, you will select a research paper, read it and present it within the research seminar (10-15 min presentation). Instead of selecting a paper of our list, you can also suggest a paper. This suggestion has to be discussed with Univ.-Prof. Dr. Elmar Rueckert first.
After the presentation, the paper is discussed for 10-15 min.
Further, external presenters that are leading researchers in AI will be invited. External speakers will present their research in 30-45 min, followed by a 15 min discussion.
Links and Resources
Location & Time
- Location: HS Thermoprozesstechnik (HS TPT) with some exceptions, see the list of dates below.
- Dates: On selected Wednesdays 12:15-14:00. Talks will be announced via MUOnline. Note: Therefore, it is important to register for the course.
List of Talks and Dates
- 25.10.2023 12:15 (HS TPT)
- Tutorial: Björn Ellensohn, Docker and other Cloud Services for research and teaching.
- 22.11.2023 12:15 (HS TPT)
- Research Talk: Dr. Ozan Özdenizci (TU Graz), on robust and secure deep learning.
Available Research Papers to Select
- Own research topics.
- Important tutorials.
- Predictive Whittle Networks for Time Series by Yu et al. (UAI 2022)
- Natural Gradient Shared Control by Oh, Wu, Toussaint and Mainprice
- Learning to solve sequential physical
reasoning problems from a scene image by Driess, Ha and Toussaint (IJRR 2021)
- Deep Visual Constraints: Neural Implicit Models for Manipulation
Planning from Visual Input by Ha, Driess and Toussaint
- AW-Opt: Learning Robotic Skills with Imitation and
Reinforcement at Scale by Lu et al. (CoRL 2021)
- Decision Transformer: Reinforcement Learning via Sequence Modeling (NeurIPS 2021) by L. Chen et al
- CURL: Contrastive Unsupervised Representations for Reinforcement Learning (ICML 2020) by A. Srinivas et al
- Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble (CoRL 2021) by S. Lee et al
- Deep Hierarchical Planning from Pixels (preprint) by D. Hafner et al
- Offline Meta-Reinforcement Learning with Online Self-Supervision by Pong et al. (ICML 2022)
- Information is Power: Intrinsic Control via Information Capture by Rhinehart et al. (NeurIPS 2021)
- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks by Finn et al.
- Event-based Asynchronous Sparse Convolutional Networks by Messikommer et al.
- Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image Statistics by Wang et al.
- A Spiking Neural Network Model of Depth from Defocus for Event-based Neuromorphic Vision by Haessig et al.
- Dream to Control: Learning Behaviors by Latent Imagination by Hafner et al.