190.003 CPS Research Seminar I (2SH SE, SS)
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.
Location & Time
- Location: HS Kuppelwieser
- Dates: Thursdays 12:00-14:00, with some exceptions, see here the list of the dates.
List of Talks and Dates
The specified time windows do not include discussions.
- 02.03.23 12:00
- Univ.-Prof. Dr. Elmar Rueckert, Introductory Slides.
- 02.03.23 12:00
- M.Sc. thesis: XX on XX.
- B.Sc. thesis: XX on XX.
- Internship Project: XX on XX.
- Ph.D. research talk: XX on XX.
- 09.03.23 12:00
- 13.03.23 12:00
- 20.03.23 12:00
- 12:00 – 12:20 M.Sc. thesis: Improving Fundamental Metallurgical Processes using Data Science Methods by Daniel Wagermaier
- 27.03.23 12:00
- 17.04.23 12:00
- 22.05.23 10:00
- 05.06.23 12:00
- 12:00 – 12:30 Research Talk:Understanding why SLAM algorithms fail in modern indoor environments by Linus Nwankwo, M.Sc.
- 12.06.23 12:00
- 19.06.23 12:00 [ONLINE]
- Research Talk: Temporal Difference Learning for Model Predictive Control’ (Model-based RL, https://arxiv.org/pdf/2203.04955.pdf by Honghu Xue, M.Sc.
- 12:00 – 12:30 Research Talk: Bayesian Optimization for learning optimal parameters of Electronic Control Units (ECU’s) for Motorcycles by Christopher Martin Shimmin, B.Sc.
Some Research Paper Candidates
- 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
- 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.