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Open Call for “Government of Ireland Postgraduate Scholarship Programme 2019”

The Irish Research Council (IRC) has published a new call for its “Government of Ireland Postgraduate Scholarship” programme. We are eligible to supervise applicants during their application process and act as PhD supervisor for the 4-year period of the scholarship. Hence, if you are interested in doing a PhD at Trinity College Dublin in the field of machine learning or recommender systems, or any other of our research fields, please contact us. From their website The Government of Ireland Postgraduate Scholarship Programme is an established national initiative, funded by the Department of Education and Skills and managed by the Council. In 2017, we invested in a total of 1,179 postgraduate scholars, with over 5,000 individual scholarships for excellent research awarded Read more…

Machine Learning

3rd Call for EU Marie Curie EDGE Fellowships (e.g. in Machine-Learning or Recommender-Systems Research)

The 3rd call for EU Marie Curie EDGE fellowships has been published. We successfully supported the application of one EDGE fellow a year ago, and we would be happy to support talented candidates this year, too. So, if you are interested in an EDGE fellowship in the field of machine learning, recommender systems or another of our research areas, please contact us. Let us know some details about your yourself and your project idea. EDGE is Marie Skłodowska-Curie COFUND Action, led by Trinity College Dublin on behalf of a group of academic institutions from across Ireland. EDGE will offer 71 prestigious Fellowships for experienced researchers (post-doctoral or equivalent) relocating to Ireland. EDGE is also a training and development programme for scientific excellence, offering a Read more…

Machine Learning

Trinity College Dublin seeks to hire a Professor (Chair) in Intelligent Systems

The University of Dublin, Trinity College, invites applications for the position of Professor of Intelligent Systems. The successful candidate will provide strong academic leadership in research, teaching and supervision. The Professor will strengthen the strategic research areas of Artificial Intelligence and Intelligent Content in the School of Computer Science and Statistics, and provide additional leadership in the SFI Research Centre for Digital Content Technology – ADAPT (www.adaptcentre.ie), hosted in the School. It is essential that the successful candidate will be an internationally recognized scholar in at least one of the following research areas: artificial intelligence; digital media and content analytics; knowledge and data engineering. These areas include scientific areas such as: Machine Learning; Natural Language Processing; Semantic Modelling; Personalisation; Data Read more…

Machine Learning

ParsRec: Meta-Learning Recommendations for Bibliographic Reference Parsing (Pre-Print)

We are delighted to announce that our poster “ParsRec: Meta-Learning Recommendations for Bibliographic Reference Parsing” has been accepted at the 12th ACM Recommender Systems Conference (RecSys) for presentation in Vancouver, Canada. The pre-print is available on arXiv, and here in our blog: Abstract Bibliographic reference parsers extract metadata (e.g. author names, title, year) from bibliographic reference strings. No reference parser consistently gives the best results in every scenario. For instance, one tool may be best in extracting titles, and another tool in extracting author names. In this paper, we address the problem of reference parsing from a recommender-systems perspective. We propose ParsRec, a meta-learning approach that recommends the potentially best parser(s) for a given reference string. We evaluate ParsRec on 105k Read more…

Machine Learning

A Stable Neural-Turing-Machine (NTM) Implementation (Source Code and Pre-Print)

We have released an open source implementation of a Neural Turing Machine for TensorFlow, and published on arXiv the corresponding paper which we will present at ICANN 2018. Neural Turing Machines are notoriously difficult to implement and train. Our contribution is to explain why previous implementations have not successfully replicated the results in the original Neural Turing Machines paper. We then went on and produced an open source implementation which trains reliably and quickly. The work was done as part of my undergraduate thesis in the group of Prof Joeran Beel at the School of Computer Science and Statistics and the ADAPT Centre at Trinity College Dublin. We published the implementation and pre-print only a few days ago, and have received considerable interest Read more…

Recommender Systems

Mr. DLib’s Living Lab for Scholarly Recommendations (preprint)

We published a manuscript on arXiv about the first living lab for scholarly recommender systems. This lab allows recommender-system researchers to conduct online evaluations of their novel algorithms for scholarly recommendations, i.e., research papers, citations, conferences, research grants etc. Recommendations are delivered through the living lab´s API in platforms such as reference management software and digital libraries. The living lab is built on top of the recommender system as-a-service Mr. DLib. Current partners are the reference management software JabRef and the CORE research team. We present the architecture of Mr. DLib’s living lab as well as usage statistics on the first ten months of operating it. During this time, 970,517 recommendations were delivered with a mean click-through rate of 0.22%. Read more…

Recommender Systems

RARD II: The 2nd Related-Article Recommendation Dataset (preprint)

We released a new version of RARD, i.e. RARD II and describe the new release in a preprint published on arXiv. The dataset is available at http://data.mr-dlib.org and the new manuscript is available on arXiv and here in our Blog. The main contribution of this paper is to introduce and describe a new recommender-systems dataset (RARD II). It is based on data from a recommender-system in the digital library and reference management software domain. As such, it complements datasets from other domains such as books, movies, and music. The RARD II dataset encompasses 89m recommendations, covering an item-space of 24m unique items. RARD II provides a range of rich recommendation data, beyond conventional ratings. For example, in addition to the usual rating Read more…

Partnerships

2-week visit at the NII Tokyo: Dagstuhl-Seminar Presentation, Recommender-System and Machine-Learning Research, …

From the 19th of June until the 2nd of July 2018, I am in Japan at the National Institute of Informatics (NII). It is my first visit as Visiting Professor at the NII, and I am working with Professor Akiko Aizawa on a recommender-system and machine-learning project. Besides that work, there are many interesting presentations and other visitors at the NII. Among others, Prof. Dr. Raimund Seidel is visiting. Prof. Seidel is the organizer of the prestigious Dagstuhl seminar. Dagstuhl enables leading researchers in computer science meet for a few days to discuss the latest advancements and challenges in their fields. The NII is organizing a similar seminar series, the Shonan seminars, and Prof. Seidel shared his experience with organizing the Dagstuhl in a Read more…

Partnerships

MoU between the National Institute of Informatics (NII) and Trinity College Dublin (TCD) / SCSS / ADAPT

After being appointed as a visiting professor at the National Institute of Informatics (NII) a few weeks ago, there is more good news. The NII has signed a memorandum of understanding (MoU) with the School of Computer Science and Statistics (SCSS) of Trinity College Dublin (TCD) and the ADAPT Research Centre. The MoU originates from our long-term collaboration with the NII and outlines a partnership between TCD/SCSS/ADAPT and the NII. The goal of the partnership is to: Engage in joint efforts in research Work together in the area of institute management Exchange administrative and managerial staff Collaborate with industry and agencies for public administration and public services Exchange of research and academic staff and students We begin the collaboration with two projects. First, PhD Read more…

Machine Learning

Aiur by Iris.ai: Solving the Problems of Science via the Blockchain and Artificial Intelligence

I am on the advisory board of Iris.ai, a business start-up in the field of open science, blockchain, and artificial intelligence. Iris.ai was founded in 2015 and since then has released several exciting products that have the potential to revolutionize how scientists work. With their latest project, Project Aiur, the team behind Iris.ai envisions a world where the right scientific knowledge is available at our fingertips; where all research is validated and reproducible; where interdisciplinary connections are the norm; where unbiased scientific information flows freely; where research already paid for with our tax money is freely accessible to all. To realize Project Aiur, Iris.ai sells “AIUR tokens”, a new currency that could be the central currency in a new world of Read more…

Machine Learning

DAAD Artificial Intelligence / Machine Learning Tour Through Germany for Postdocs from Outside Germany

The German Academic Exchange Service (DAAD) organizes the “Postdoctoral Researchers’ Networking AI Tour” 2018 to offer on-site visits to universities, research institutes and companies in the field of artificial intelligence, discussions with experts and numerous networking opportunities. Programme-related costs and a travel allowance are covered by DAAD. We have cooperated and received funding from the DAAD on numerous occasions, e.g. to participate in conferences, receive interns, or conduct research visits abroad e.g. in Cyprus and Tokyo. We were always pleased by the professional organization of DAAD events and great networking opportunities that DAAD offered. As such, we can highly recommend applying for the DAAD Artificial Intelligence Tour through Germany, if you are a postdoc from outside Germany who considers working and living Read more…