Alpherts, T., Ghebreab, S., Hsu, Y.-C., & Van Noord, N. (2024). Perceptive Visual Urban Analytics is Not (Yet) Suitable for Municipalities. In ACM FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency : June 3rd-6th 2024, Rio de Janeiro, Brazil (pp. 1341-1354). Association for Computing Machinery. https://doi.org/10.1145/3630106.3658976[details]
Dankloff, M., Skoric, V., Sileno, G., Ghebreab, S., Ossenbruggen, J. V., & Beauxis-Aussalet, E. (2024). Analysing and organising human communications for AI fairness assessment: Use cases from the Dutch Public Sector. AI and Society. Advance online publication. https://doi.org/10.1007/s00146-024-01974-4
Skoric, V., Sileno, G., & Ghebreab, S. (2024). Roles of Standardised Criteria in Assessing Societal Impact of AI. In Proceedings - 2024 IEEE Conference on Artificial Intelligence, CAI 2024 (pp. 1240-1245). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAI59869.2024.00220
Škorić, V., Ghebreab, S., & Sileno, G. (2024). Critical Criteria for AI Impact Assessment: A Proposal, Applied on Current Standards. Journal of AI Law and Regulation, 1(3), 281 - 296. https://aire.lexxion.eu/article/AIRE/2024/3/5
2022
Skoric, V., Sileno, G., & Ghebreab, S. (2022). Legality, Legitimacy, and Instrumental Possibility in Human and Computational Governance for the Public Sector. In P. Feletig, A. Loreggia, & S. Quintarelli (Eds.), Proceedings of the Second International Forum on Digital and Democracy: November 17-18, 2022, live and digital event in Rome, Italy Article 10 (CEUR Workshop Proceedings; Vol. 3289). CEUR-WS. https://ceur-ws.org/Vol-3289/paper4.pdf[details]
Groen, I. I. A., Jahfari, S., Seijdel, N., Ghebreab, S., Lamme, V. A. F., & Scholte, H. S. (2018). Scene complexity modulates degree of feedback activity during object detection in natural scenes. PLoS Computational Biology, 14(12), Article e1006690. Advance online publication. https://doi.org/10.1101/293290, https://doi.org/10.1371/journal.pcbi.1006690[details]
Alnajar, F., Gevers, T., Valenti, R., & Ghebreab, S. (2017). Auto-Calibrated Gaze Estimation Using Human Gaze Patterns. International Journal of Computer Vision, 124(2), 223-236. Advance online publication. https://doi.org/10.1007/s11263-017-1014-x[details]
Groen, I. I. A., Ghebreab, S., Lamme, V. A. F., & Scholte, H. S. (2016). The time course of natural scene perception with reduced attention. Journal of Neurophysiology, 115(2), 931-946. Advance online publication. https://doi.org/10.1152/jn.00896.2015[details]
Tamboer, P., Vorst, H. C. M., Ghebreab, S., & Scholte, H. S. (2016). Machine learning and dyslexia: Classification of individual structural neuro-imaging scans of students with and without dyslexia. NeuroImage: Clinical, 11, 508-514. https://doi.org/10.1016/j.nicl.2016.03.014[details]
Ramakrishnan, K., Scholte, H. S., Groen, I. I. A., Smeulders, A. W. M., & Ghebreab, S. (2015). Visual dictionaries as intermediate features in the human brain. Frontiers in Computational Neuroscience, 8, Article 168. https://doi.org/10.3389/fncom.2014.00168[details]
Ramakrishnan, K., Scholte, H. S., Smeulders, A. W. M., & Ghebreab, S. (2015). Neural spatial consistency of hierarchical vision models. In NIPS Workshop on Machine Learning in Neuroimaging https://sites.google.com/site/mliniworkshop2015/schedule
2014
Ramakrishnan, K., Groen, I. I. A., Scholte, H. S., Smeulders, A. W. M., & Ghebreab, S. (2014). Visual dictionaries in the Brain: Comparing HMAX and BOW. In 2014 IEEE International Conference on Multimedia and Expo (ICME 2014): Chengdu, China 14-18 July 2014 IEEE. https://doi.org/10.1109/ICME.2014.6890312[details]
Alnajar, F., Gevers, T., Valenti, R., & Ghebreab, S. (2013). Calibration-Free Gaze Estimation Using Human Gaze Patterns. In 2013 IEEE International Conference on Computer Vision: ICCV 2013 : proceedings: 1-8 December 2013, Sydney, NSW, Australia (pp. 137-144). IEEE Computer Society. https://doi.org/10.1109/ICCV.2013.24[details]
Groen, I. I. A., Ghebreab, S., Prins, H., Lamme, V. A. F., & Scholte, H. S. (2013). From image statistics to scene gist: evoked neural activity reveals transition from low-level natural image structure to scene category. The Journal of Neuroscience, 33(48), 18814-18824. Advance online publication. https://doi.org/10.1523/JNEUROSCI.3128-13.2013[details]
Groen, I. I. A., Ghebreab, S., Lamme, V. A. F., & Scholte, H. S. (2012). Low-level contrast statistics are diagnostic of invariance of natural textures. Frontiers in Computational Neuroscience, 6, 34. Article 34. https://doi.org/10.3389/fncom.2012.00034[details]
Groen, I. I. A., Ghebreab, S., Lamme, V. A. F., & Scholte, H. S. (2012). Spatially pooled contrast responses predict neural and perceptual similarity of naturalistic image categories. PLoS Computational Biology, 8(10), Article e1002726. https://doi.org/10.1371/journal.pcbi.1002726.g001[details]
Ghebreab, S., & Smeulders, A. W. M. (2011). Identifying distributed and overlapping clusters of hemodynamic synchrony in fMRI data sets. Pattern Analysis and Applications, 14(2), 175-192. Advance online publication. https://doi.org/10.1007/s10044-010-0186-6[details]
Ghebreab, S., Smeulders, A. W. M., Scholte, H. S., & Lamme, V. A. F. (2010). A biologically plausible model for rapid natural scene identification. In Y. Bengio, D. Schuurmans, J. Lafferty, C. Williams, & A. Culotta (Eds.), 23rd Annual Conference on Neural Information Processing Systems 2009: December 7-10, 2009, Vancouver, B.C., Canada (Vol. 1, pp. 629-637). (Advances in Neural Information Processing Systems; Vol. 22). Curran. https://papers.nips.cc/paper/3785-a-biologically-plausible-model-for-rapid-natural-scene-identification[details]
2009
Scholte, H. S., Ghebreab, S., Waldorp, L., Smeulders, A. W. M., & Lamme, V. A. F. (2009). Brain responses strongly correlate with Weibull image statistics when processing natural images. Journal of Vision, 9(4), Article 29. https://doi.org/10.1167/9.4.29[details]
Ramakrishnan, K., Scholte, H. S., Smeulders, A., & Ghebreab, S. (2016). Mapping human visual representations by deep neural networks. Journal of Vision, 16(12), Article 373. https://doi.org/10.1167/16.12.373[details]
Michailidis, D., Ghebreab, S., & Pascoal Dos Santos, F. P. (2023). Balancing Fairness and Efficiency in Transport Network Design through Reinforcement Learning. https://doi.org/10.5555/3545946.3598992
Škorić, V., Sileno, G., & Ghebreab, S. (2023). Human rights in AI impact assessment: insights from pilot implementations, regulatory frameworks, and a proposal. Paper presented at LAWTOMATION DAYS Second Edition, Madrid, Spain.
Škorić, V., Sileno, G., & Ghebreab, S. (2023). Leveraging public procurement for LLMs in the public sector: Enhancing access to justice responsibly. Paper presented at JURIX Workshop on AI and Access to Justice , Maastricht, Netherlands.
Groen, I., Ghebreab, S., Lamme, V. A. F., & Scholte, H. S. (2013). Neural Dissimilarity of Scene Naturalness Maps Onto Low-Level Contrast Statistics. Abstract from 19th Annual Meeting of the Organization for Human Brain Mapping, Seattle, Washington, United States. http://www.science.uva.nl/research/publications/2013/GroenHBM2013
Groen, I., Ghebreab, S., Lamme, V. A. F., & Scholte, H. S. (2013). Two Stages in Scene Gist Perception. Abstract from 2013 Annual Meeting of the Vision Sciences Society, Naples, Florida, United States. http://www.science.uva.nl/research/publications/2013/GroenVSS2013
Ramakrishnan, K., Scholte, S., Lamme, V., Smeulders, A., & Ghebreab, S. (2015). Convolutional Neural Networks in the Brain: an fMRI study. Journal of Vision, 15(12), Article 371. https://doi.org/10.1167/15.12.371
2014
Groen, I., Ghebreab, S., Lamme, V. A. F., & Scholte, S. (2014). Neural computation of scene gist with and without attention. Journal of Vision, 14(10), 884. https://doi.org/10.1167/14.10.884
Scholte, H. S., & Ghebreab, S. (2014). Improving computational models of early visual cortex using single image ERP data. Journal of Vision, 14(10), 886. https://doi.org/10.1167/14.10.886
2010
Ghebreab, S., Scholte, H. S., Lamme, V., & Smeulders, A. (2010). Rapid Natural Image Identification Based on EEG Data and Global Scene Statistics. Journal of Vision, 10(7), 1394. https://doi.org/10.1167/10.7.1394
Groen, I., Ghebreab, S., Lamme, V., & Scholte, H. S. (2010). The role of Weibull image statistics in rapid object detection in natural scenes. Journal of Vision, 10(7), 992. https://doi.org/10.1167/10.7.992
Scholte, H. S., Ghebreab, S., Smeulders, A., & Lamme, V. (2010). Lateral Occipital cortex responsive to correlation structure of natural images. Journal of Vision, 10(7), 1363. https://doi.org/10.1167/10.7.1363
Scholte, H. S., Ghebreab, S., Smeulders, A., & Lamme, V. (2010). Lateral Occipital cortex responsive to local correlation structure of natural images. Frontiers in Neuroscience, Conference abstract(Computational and Systems Neuroscience 2010). https://doi.org/10.3389/conf.fnins.2010.03.00162[details]
Ghebreab, S. (invited speaker) (2012). Natural Image Statistics, Visual Perception and the Brain, Lecture, University of Groningen, The Netherlands.
Ghebreab, S. (invited speaker) (2012). Natural Scene statistics predict neural and perceptual similarity of naturalistic image categories, SURF research day, Utrecht, The Netherlands.
Ghebreab, S. (invited speaker) (2012). Predicting human response to film from visual contents: Studies in the links between computer vision and human response systems, How Movies Move Us Symposium, Tel-Aviv University, Israel.
Ghebreab, S. (invited speaker) (14-10-2011). Computer vision meets human vision: how semantic and emotional content in pictures and movies can be traced back in the brain, 2011 NeuroInformatics.NL symposium, Den Haag.
2017
Ramakrishnan, K. (2017). Aligning computer and human visual representations. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
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