Zobrazeno 1 - 10
of 323
pro vyhledávání: '"HANJALIC, ALAN"'
Publikováno v:
Published in Data-centric Machine Learning Research Worshop @ ICML 2024
Graph Neural Networks (GNNs) have achieved state-of-the-art results in node classification tasks. However, most improvements are in multi-class classification, with less focus on the cases where each node could have multiple labels. The first challen
Externí odkaz:
http://arxiv.org/abs/2406.12439
In recent years, continual learning (CL) techniques have made significant progress in learning from streaming data while preserving knowledge across sequential tasks, particularly in the realm of euclidean data. To foster fair evaluation and recogniz
Externí odkaz:
http://arxiv.org/abs/2406.01229
Nodal spreading influence is the capability of a node to activate the rest of the network when it is the seed of spreading. Combining nodal properties (centrality metrics) derived from local and global topological information respectively is shown to
Externí odkaz:
http://arxiv.org/abs/2309.12967
Mainstream bias, where some users receive poor recommendations because their preferences are uncommon or simply because they are less active, is an important aspect to consider regarding fairness in recommender systems. Existing methods to mitigate m
Externí odkaz:
http://arxiv.org/abs/2307.13632
Publikováno v:
Transaction Of Machine Learning Research, 2835-8856, 2023
Graph Neural Networks (GNNs) have shown state-of-the-art improvements in node classification tasks on graphs. While these improvements have been largely demonstrated in a multi-class classification scenario, a more general and realistic scenario in w
Externí odkaz:
http://arxiv.org/abs/2304.10398
Autor:
Subramanyam, Shishir, Viola, Irene, Jansen, Jack, Alexiou, Evangelos, Hanjalic, Alan, Cesar, Pablo
Remote communication has rapidly become a part of everyday life in both professional and personal contexts. However, popular video conferencing applications present limitations in terms of quality of communication, immersion and social meaning. VR re
Externí odkaz:
http://arxiv.org/abs/2205.04906
Many real-world complex systems including human interactions can be represented by temporal (or evolving) networks, where links activate or deactivate over time. Characterizing temporal networks is crucial to compare such systems and to study the dyn
Externí odkaz:
http://arxiv.org/abs/2111.01506
Direct optimization of IR metrics has often been adopted as an approach to devise and develop ranking-based recommender systems. Most methods following this approach aim at optimizing the same metric being used for evaluation, under the assumption th
Externí odkaz:
http://arxiv.org/abs/2106.02545
In a collaborative-filtering recommendation scenario, biases in the data will likely propagate in the learned recommendations. In this paper we focus on the so-called mainstream bias: the tendency of a recommender system to provide better recommendat
Externí odkaz:
http://arxiv.org/abs/2102.01744