Conferences

Report from the 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Science

The 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Science (AICS) is over, and it was a full success. This year, AICS celebrated its 30th anniversary and was hosted by Trinity College Dublin’s School of Computer Science and Statistics, the School of Psychology and the Institute of Neuroscience. I had the honour to co-organize the conference together with Rob Brennan (General Co-Chair), Ruth Byrne (Cognitive Science Chair) and Jeremy Debattista (Publication Chair). AICS took place on the Trinity campus at the Trinity Long Room Hub, Trinity’s interdisciplinary arts and humanities research institute. The following text is based on [3]. While once a niche area, the fields of Cognitive Science and Artificial Intelligence, which encompass Data Analytics, Information Retrieval, and Machine Learning, are now Read more…

By Joeran Beel, ago
Machine Learning

A Novel Approach to Recommendation Algorithm Selection using Meta-Learning

Our paper “A Novel Approach to Recommendation Algorithm Selection using Meta-Learning” was accepted for publication at the 26th Irish Conference on Artificial Intelligence and Cognitive Science (AICS): Introduction  The ‘algorithm selection problem’ describes the challenge of finding the most effective algorithm for a given recommendation scenario. Some typical recommendation scenarios are news websites [3], digital libraries [4, 5], movie-streaming platforms [13]. The performance of recommender system algorithms vary in these different scenarios [3, 6, 10, 11, 15] as illustrated in Fig. 1. Performance variation occurs for many reasons, for example, the effectiveness of collaborative filtering algorithms changes depending on the number of ratings available from users [10]. Algorithms also perform differently depending on the demographic characteristics of users [6][11], depending on Read more…

By Andrew Collins, ago
Machine Learning

Accepted Workshop @ECIR2019: The 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR)

Our proposal for the “1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR)“, to be held at the  41st European Conference on Information Retrieval (ECIR), was accepted. AMIR will take place on the 14th of April 2019 in Cologne, Germany. The algorithm selection problem describes the challenge of identifying the best algorithm for a given problem space. In many domains, particularly artificial intelligence, the algorithm selection problem is well studied, and various approaches and tools exist to tackle it in practice. Especially through meta-learning impressive performance improvements have been achieved. The information retrieval (IR) community, however, has paid little attention to the algorithm selection problem, although the problem is highly relevant in information retrieval. AMIR will bring Read more…

By Joeran Beel, ago
Machine Learning

Best-Paper Award for our Publication “Implementing Neural Turing Machines” at the 27th International Conference on Artificial Neural Networks

We received the best-paper award at the 27th International Conference on Artificial Neural Networks (ICANN 2018) for our paper Implementing Neural Turing Machines. Our student, and co-author of the paper, Mark Collier was at ICANN to present our work about implementing a Neural Turing Machine. Neural Turing Machines (NTMs) are an instance of Memory Augmented Neural Networks, a new class of recurrent neural networks which decouple computation from memory by introducing an external memory unit. Neural Turing Machines have demonstrated superior performance over Long Short-Term Memory Cells in several sequence learning tasks. A number of open source implementations of Neural Turing Machines exist but are unstable during training and/or fail to replicate the reported performance of NTMs. This paper presents the details Read more…

By Joeran Beel, ago
Machine Learning

An Empirical Comparison of Syllabuses for Curriculum Learning (Pre-Print)

Update 12/11/2018: Our paper has been accepted at AICS 2018 and will be presented at the conference in December. We have published a pre-print (now available on Arxiv) which outlines our work comparing different syllabuses for curriculum learning. Neural networks are typically trained by repeatedly randomly selecting examples from a dataset and taking steps of stochastic gradient descent. Curriculum learning is an alternative approach to training neural networks, inspired by human learning in which training examples are presented according to a syllabus typically of increasing “difficulty”. Curriculum learning has shown some impressive empirical results, but little is known about the relative merits of different syllabuses. In this work, we provide an empirical comparison of a number of syllabuses found in Read more…

By Mark Collier, ago
Machine Learning

Proposal for the 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR)

Lars Kotthoff and I have applied to organize the 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR) at the 41st European Conference on Information Retrieval (ECIR). Let’s cross fingers and hope it will get accepted. In the following, you find the proposal (also available on ResearchGate as PDF). @InProceedings{BeelKotthoff2018, author = {Beel, Joeran and Kotthoff, Lars}, title = {Proposal for the 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR)}, booktitle = {ResearchGate Repository}, year = {2018}, pages = {1–6}, doi = {10.13140/RG.2.2.14548.65922}, url = {https://www.researchgate.net/publication/328965675_Proposal_for_the_1st_Interdisciplinary_Workshop_on_Algorithm_Selection_and_Meta-Learning_in_Information_Retrieval_AMIR}, abstract = {The algorithm selection problem describes the challenge of identifying the best algorithm for a given problem space. In many domains, particularly artificial intelligence, the algorithm selection problem Read more…

By Joeran Beel, ago
Jobs & Internships

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…

By Joeran Beel, ago
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…

By Joeran Beel, ago
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…

By Joeran Beel, ago