50 citations · 61 across the 3 of their papers we have counts for
7 papers
Across-Game Engagement Modelling via Few-Shot Learning
Kosmas Pinitas, Konstantinos Makantasis, Georgios N. Yannakakis
Domain generalisation involves learning artificial intelligence (AI) models that can maintain high performance across diverse domains within a specific task. In video games, for in…
The Invariant Ground Truth of Affect
Konstantinos Makantasis, Kosmas Pinitas, Antonios Liapis +1
Affective computing strives to unveil the unknown relationship between affect elicitation, manifestation of affect and affect annotations. The ground truth of affect, however, is p…
RankNEAT: Outperforming Stochastic Gradient Search in Preference Learning Tasks
Kosmas Pinitas, Konstantinos Makantasis, Antonios Liapis +1
Stochastic gradient descent (SGD) is a premium optimization method for training neural networks, especially for learning objectively defined labels such as image objects and events…
AffRankNet+: Ranking Affect Using Privileged Information
Konstantinos Makantasis
Many of the affect modelling tasks present an asymmetric distribution of information between training and test time; additional information is given about the training data, which…
Privileged Information for Modeling Affect In The Wild
Konstantinos Makantasis, David Melhart, Antonios Liapis +1
A key challenge of affective computing research is discovering ways to reliably transfer affect models that are built in the laboratory to real world settings, namely in the wild.…
The Pixels and Sounds of Emotion: General-Purpose Representations of Arousal in Games
Konstantinos Makantasis, Antonios Liapis, Georgios N. Yannakakis
What if emotion could be captured in a general and subject-agnostic fashion? Is it possible, for instance, to design general-purpose representations that detect affect solely from…