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  1. Training Machine Learning Models to Detect Group Differences in Neuro…

    Guglielmo, G., Wiltshire, T., & Louwerse, M. (Accepted/In press). Training Machine Learning Models to Detect Group Differences in Neurophysiological Data Using Recurrence Quantification Analysis Based Features. In 14th International Conference on Agents and Artificial Intelligence (ICAART)
  2. Effect of stimuli congruency on gaze behavior and memory

    Kraeter, V. A., Kritskaia, V., Wiltshire, T., & Collin, S. H. P. (2022). Effect of stimuli congruency on gaze behavior and memory. In Effect of stimuli congruency on gaze behavior and memory https://escholarship.org/uc/item/2j87q9kf
  3. Neural Data-to-Text Generation Based on Small Datasets - Comparing th…

    van der Lee, C., Ferreira, T., Emmery, C., Wiltshire, T., & Krahmer, E. (2022). Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model. Manuscript submitted for publication.
  4. Prospects for Augmenting Team Interactions with Real-Time Coordinatio…

    Wiltshire, T., van Eijndhoven, K., Halgas, E., & Gevers, J. (2022). Prospects for Augmenting Team Interactions with Real-Time Coordination-Based Measures in Human-Autonomy Teams. Topics in Cognitive Science. https://onlinelibrary.wiley.com/doi/10.1111/tops.12606
  5. Human Interaction and Networking Transitions System (HINTS) for Socia…

    Doan, N., Hudson, D., Wiltshire, T., Lijdsman, P., Wever, S., & Atzmueller, M. (2021). Human Interaction and Networking Transitions System (HINTS) for Social User Analytics and Modeling of Offline Team Group Interaction Information. In Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (UMAP '21 Adjunct) https://camps.aptaracorp.com/ACM_PMS/PMS/ACM/UMAP21ADJUNCT/29/680f25d7-a38f-11eb-8d84-166a08e17233/OUT/umap21adjunct-29.pdf

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