Proceedings of the Second International Workshop on Multimodal Immersive Learning Systems (MILeS 2022)
At the Seventeenth European Conference on Technology Enhanced Learning (EC-TEL 2022)
Toulouse, France, September 12th-16th, 2022.
The 1st Workshop on Philosophy of Learning Analytics (POLA) at the 11th International Conference on Learning Analytics and Knowledge (LAK21). Online April 12/13, 2021
Methodenset für Online-Veranstaltungen und -Workshops: Mit diesen Vertrauenskarten und Take-A-Break-Karten Webinare interaktiv und motivierend gestalten!
- Aug. 19 – Aug. 28, 2020
- Nike Sun (Massachusetts Institute of Technology; chair), Jian Ding (University of Pennsylvania), Ronen Eldan (Weizmann Institute), Elchanan Mossel (Massachusetts Institute of Technology), Joe Neeman (University of Texas at Austin), Jelani Nelson (UC Berkeley), Tselil Schramm (Stanford University; Microsoft Research Fellow)
- Sep. 28 – Oct. 2, 2020
- Lihong Li (Google Brain; chair), Marc G. Bellemare (Google Brain)
- The success of deep neural networks in modeling complicated functions has recently been applied by the reinforcement learning community, resulting in algorithms that are able to learn in environments previously thought to be much too large. Successful applications span domains from robotics to health care. However, the success is not well understood from a theoretical perspective. What are the modeling choices necessary for good performance, and how does the flexibility of deep neural nets help learning? This workshop will connect practitioners to theoreticians with the goal of understanding the most impactful modeling decisions and the properties of deep neural networks that make them so successful. Specifically, we will study the ability of deep neural nets to approximate in the context of reinforcement learning.
- Aug. 31 – Sep. 4, 2020
- Csaba Szepesvari (University of Alberta, Google DeepMind; chair), Emma Brunskill (Stanford University), Sébastien Bubeck (MSR), Alan Malek (DeepMind), Sean Meyn (University of Florida), Ambuj Tewari (University of Michigan), Mengdi Wang (Princeton)
This program aims to reunite researchers across disciplines that have played a role in developing the theory of reinforcement learning. It will review past developments and identify promising directions of research, with an emphasis on addressing existing open problems, ranging from the design of efficient, scalable algorithms for exploration to how to control learning and planning. It also aims to deepen the understanding of model-free vs. model-based learning and control, and the design of efficient methods to exploit structure and adapt to easier environments.
The program focused on the following four themes:
- Optimization: How and why can deep models be fit to observed (training) data?
- Generalization: Why do these trained models work well on similar but unobserved (test) data?
- Robustness: How can we analyze and improve the performance of these models when applied outside their intended conditions?
- Generative methods: How can deep learning be used to model probability distributions?
Machine Learning Summer School (MLSS) is a course about modern methods of statistical machine learning and inference. It presents topics which are at the cor...
Wondering what the outcomes of a Mock xAPI Project are? Check how to explore the outcomes of a Mock xAPI Project and create great experiences for learners.
The SHEILA project team ran a workshop on “Developing an evidence-based institutional learning analytics policy” at the 13th European Conference on Technology Enhanced Learning on 3 September at the University of Leeds.
ABC is an effective and engaging hands-on workshop that has now been trialled with great success over a range of programmes. In just 90 minutes using a game format teams are able to work together to create a visual ‘storyboard’ outlining the type and sequence of learning activities (both online and offline) required to meet the module’s learning outcomes. ABC is particularly useful for new programmes or those changing to an online or more blended format.
1st Part: "Prof. Bruns, Prof. Burguess & Dr. Woodford: Mapping Online Publics: New Methods for Twitter Research"
2nd Part: "Robert Jäschke: Identifying and Analyzing Researchers on Twitter"
This year's discovery challenge presents two tasks in the new area
of social bookmarking. One task covers spam detection and
the other covers tag recommendations. As we are hosting the social bookmark and
publication sharing system BibSonomy, we are able to provide a dataset
of BibSonomy for the challenge. A training dataset for both tasks is provided at the beginning of the competition.
The test dataset will be released 48 hours before the final deadline. Due to a very tight schedule we cannot grant any deadline
extension.
The presentation of the results will take place at the ECML/PKDD workshop where the top teams are
invited to present their approaches and results.
mendation service which can be called via HTTP by BibSonomy's recommender when a user posts a bookmark or publication. All participating recommenders are called on each posting process, one of them is choosen to actually deliver the results to the user. We can then measure
Sofern du hier schon eine Weile mitliest, wird dir wahrscheinlich nicht entgangen sein, dass ich ein großer Fan von Blogparaden bin. Ich nehme gerne an Blogparaden teil und ich veranstalte auch welche selbst. Derzeit läuft gerade wieder eine hier im Blog: Mein wichtigster Traffictipp für Blogger. Doch was sind die Gründe dafür, dass ich ein…
1st Part: "Prof. Bruns, Prof. Burguess & Dr. Woodford: Mapping Online Publics: New Methods for Twitter Research"
2nd Part: "Robert Jäschke: Identifying and Analyzing Researchers on Twitter"