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 the work. 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. NTMs have demonstrated superior performance over Long Short-Term Memory Cells in several sequence learning tasks. A number of open source implementations of NTMs exist but are unstable during training and/or fail to replicate the reported performance of NTMs. This paper presents the details of our successful implementation of a NTM. Our implementation learns Read more…

By Joeran Beel, ago
Machine Learning

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

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 the literature. Abstract Syllabuses for curriculum learning have been developed on an ad-hoc, per task basis and little is Read more…

By Mark Collier, 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
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…

By Joeran Beel, ago
Machine Learning

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

Update 2018-10-20: Official publication available at SpringerLink Update: 2018-10-06: Our paper received the best-paper award at ICANN.   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 Read more…

By Mark Collier, ago
Machine Learning

Teaching

All lecture materials (lecture slides, assignments, exams) and the latest news are available on  Machine Learning (CS7CS4/CS4404) Students who complete this module should be able to (1) Decide when machine learning is an appropriate method to solve a problem (2) Understand how machine learning works. (3) Apply machine-learning frameworks to solve real-world problems, and adjust and extend existing algorithms when necessary. The module content includes Machine Learning Basics (Application Areas, Challenges, Alternatives to Machine Learning) Machine Learning in Action (Datasets, Frameworks, Evaluation) Cross-validation and confidence intervals Overfitting/underfitting (bias-variance trade-off) Machine Learning Algorithms Linear Regression Logistic Regression Support Vector Machines Kernel Methods k-Means Clustering and Mixture Models for Unsupervised Learning Neural Networks Deep Learning Algorithms Use of gradient descent, and extensions for Read more…

By Joeran Beel, ago
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…

By Joeran Beel, ago
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…

By Joeran Beel, ago