no code implementations • 25 May 2023 • Vasileios Moschopoulos, Pantelis Kyriakidis, Aristotelis Lazaridis, Ioannis Vlahavas
A successful tactic that is followed by the scientific community for advancing AI is to treat games as problems, which has been proven to lead to various breakthroughs.
no code implementations • CRAC (ACL) 2021 • Nikolaos Stylianou, Ioannis Vlahavas
Language Models are the underpin of all modern Natural Language Processing (NLP) tasks.
no code implementations • 11 Oct 2021 • Aristotelis Lazaridis, Ioannis Vlahavas
In an effort to patch these issues, we integrated a meta-learning technique in order to shift the objective of learning to solve a task into the objective of learning how to learn to solve a task (or a set of tasks), which we empirically show that improves overall stability and performance of Deep RL algorithms.
no code implementations • COLING (CRAC) 2020 • Nikolaos Stylianou, Ioannis Vlahavas
In this paper we present an extension over the Transformer-block architecture used in neural language models, specifically in GPT2, in order to incorporate entity annotations during training.
no code implementations • 22 Mar 2020 • Eleftherios Spyromitros-Xioufis, Konstantinos Sechidis, Ioannis Vlahavas
Multi-target regression is concerned with the prediction of multiple continuous target variables using a shared set of predictors.
no code implementations • 20 Nov 2019 • Ioannis Mollas, Nick Bassiliades, Ioannis Vlahavas, Grigorios Tsoumakas
Towards a future where machine learning systems will integrate into every aspect of people's lives, researching methods to interpret such systems is necessary, instead of focusing exclusively on enhancing their performance.
no code implementations • 21 Oct 2019 • Nikolaos Stylianou, Ioannis Vlahavas
We highlight the advantages and disadvantages of the approaches, the challenges of the task, the lack of agreed-upon standards in the task and propose a way to further expand the boundaries of the field.
no code implementations • 28 Jul 2017 • Anestis Fachantidis, Matthew E. Taylor, Ioannis Vlahavas
In this article we study the transfer learning model of action advice under a budget.
no code implementations • 18 Apr 2017 • Yannis Papanikolaou, Grigorios Tsoumakas, Manos Laliotis, Nikos Markantonatos, Ioannis Vlahavas
Background: In this paper we present the approaches and methods employed in order to deal with a large scale multi-label semantic indexing task of biomedical papers.
no code implementations • 21 Oct 2014 • Thanos G. Stavropoulos, Stelios Andreadis, Nick Bassiliades, Dimitris Vrakas, Ioannis Vlahavas
The algorithm is hybrid in nature, combining novel and known concepts, such as a logic-based strategy and syntactic text-similarity measures on semantic annotations and textual descriptions.
no code implementations • 20 Apr 2014 • Grigorios Tsoumakas, Eleftherios Spyromitros-Xioufis, Aikaterini Vrekou, Ioannis Vlahavas
Multi-target regression is concerned with the simultaneous prediction of multiple continuous target variables based on the same set of input variables.
no code implementations • 28 Nov 2012 • Eleftherios Spyromitros-Xioufis, Grigorios Tsoumakas, William Groves, Ioannis Vlahavas
When the prediction targets are binary the task is called multi-label classification, while when the targets are continuous the task is called multi-target regression.