All Outputs (5)

Migrating Models: A Decentralized View on Federated Learning (2021)
Presentation / Conference Contribution
Kiss, P., & Horváth, T. (2021, September). Migrating Models: A Decentralized View on Federated Learning. Presented at ECML PKDD 2021, Online

Federated learning (FL) researches attempt to alleviate the increasing difficulty of training machine learning models, when the training data is generated in a massively distributed way. The key idea behind these methods is moving the training to loc... Read More about Migrating Models: A Decentralized View on Federated Learning.

Linear Concept Approximation for Multilingual Document Recommendation (2021)
Book Chapter
Salamon, V. T., Tashu, T. M., & Horváth, T. (2021). Linear Concept Approximation for Multilingual Document Recommendation. . Springer. https://doi.org/10.1007/978-3-030-91608-4_15

In this paper, we proposed Linear Concept Approximation, a novel multilingual document representation approach for the task of multilingual document representation and recommendation. The main idea is in creating representations by using mappings to... Read More about Linear Concept Approximation for Multilingual Document Recommendation.

Time-Series in Hyper-parameter Initialization of Machine Learning Techniques (2021)
Presentation / Conference Contribution
Horváth, T., Mantovani, R. G., & de Carvalho, A. C. P. L. F. (2021, November). Time-Series in Hyper-parameter Initialization of Machine Learning Techniques. Presented at 22nd International Conference on Intelligent Data Engineering and Automated Learning

Initializing the hyper-parameters (HPs) of machine learning (ML) techniques became an important step in the area of automated ML (AutoML). The main premise in HP initialization is that a HP setting that performs well for a certain dataset(s) will als... Read More about Time-Series in Hyper-parameter Initialization of Machine Learning Techniques.

Multimodal Emotion Recognition from Art Using Sequential Co-Attention (2021)
Journal Article
Tashu, T. M., Hajiyeva, S., & Horvath, T. (2021). Multimodal Emotion Recognition from Art Using Sequential Co-Attention. Journal of Imaging, 7(8), Article 157. https://doi.org/10.3390/jimaging7080157

In this study, we present a multimodal emotion recognition architecture that uses both feature-level attention (sequential co-attention) and modality attention (weighted modality fusion) to classify emotion in art. The proposed architecture helps the... Read More about Multimodal Emotion Recognition from Art Using Sequential Co-Attention.

Attention-Based Multi-modal Emotion Recognition from Art (2021)
Presentation / Conference Contribution
Tashu, T. M., & Horváth, T. (2021, January). Attention-Based Multi-modal Emotion Recognition from Art. Presented at ICPR 2021, Online

Emotions are very important in dealing with human decisions, interactions, and cognitive processes. Art is an imaginative human creation that should be appreciated, thought-provoking, and elicits an emotional response. The automatic recognition of em... Read More about Attention-Based Multi-modal Emotion Recognition from Art.