Rethinking supervised learning: insights from biological learning and from calling it by its name. Alex Hernandez-Garcia, 2020. Neural Information Processing Systems (NeurIPS), Workshop on Shared visual representations between humans and machines (SVRHM) OpenReview
Publications
Global visual salience of competing stimuli. Alex Hernandez-Garcia, Ricardo Ramos Gameiro, Alessandro Grillini, Peter König, 2020. Journal of Vision PsyArXiv Journal of Vision code Twitter
Learning robust visual representations using data augmentation invariance. Alex Hernandez-Garcia, Peter König, Tim C. Kietzmann, 2019. arXiv:1906.04547 arXiv code BAICS @ ICLR 2020 Video
Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT. Alex Hernandez-Garcia, Johannes Mehrer, Nikolaus Kriegeskorte, Peter König, Tim C. Kietzmann, 2018. Cognitive Computational Neuroscience (CCN) PDF
Emotion and attention: predicting electrodermal activity through video visual descriptors. Alex Hernandez-Garcia, Fernando Fernández-Martínez, Fernando Díaz de María, 2017. Int. Workshop on Affective Computing and Emotion Recognition (ACER) PDF
Exploiting visual saliency for assessing the impact of car commercials upon viewers. Fernando Fernández-Martínez, Alex Hernandez-Garcia, Miguel Ángel Fernández-Torres, Iván González-Díaz, Álvaro García-Faura, Fernando Díaz de María, 2017. Multimedia Tools and Applications paper
Comparing visual descriptors and automatic rating strategies for video aesthetics prediction. Alex Hernandez-Garcia, Fernando Fernández-Martínez, Fernando Díaz de María, 2016. Journal of Signal Processing: Image Communication paper
Succeeding metadata based annotation scheme and visual tips for the automatic assessment of video aesthetic quality in car commercials. Fernando Fernández-Martínez, Alex Hernandez-Garcia, Fernando Díaz de María, 2015. International Journal Expert Systems with Applications paper
Combining audio-visual features for viewers’ perception classification of Youtube car commercials. Fernando Fernández-Martínez, Alex Hernandez-Garcia, Ascensión Gallardo-Antolín, Fernando Díaz de María, 2014. Speech, Language and Audio in Multimedia (SLAM) paper
Talks
June 2020. Berlin Machine Learning Seminar, BerlinOnline (GermanyThe Internet). Learning robust visual representations using data augmentation invariance.
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March 2020. neuromatch 2020 1.0, Online (The Internet). Data augmentation invariance for learning robust visual representations. link
November 2019. Department of Brain and Cognitive Engineering, Korea University, Seoul (Republic of Korea). Data augmentation for improved regularization and invariance learning.
July 2019. Neural Information Processing Group, University of Tübingen, Tübingen (Germany). More than more data: undervalued advantages of data augmentation for deep learning and computational neuroscience. link
July 2019. Berlin Machine Learning Meetup, Berlin (Germany). Data augmentation: an alternative to explicit regularization and other undervalued advantages. link
June 2019. Cognitive Neuroscience Center, University Medical Center Groningen, Groningen (Netherlands). Data augmentation: undervalued advantages for deep learning and computational neuroscience.
February 2019. Neural Dynamics of Visual Cognition Lab, Freie Universität Berlin, Berlin (Germany). On the advantages of data augmentation for deep learning and computational neuroscience.
October 2018. Institute of Informatics and Telecommunications, NCSR Demokritos, Athens (Greece). Data augmentation as a biologically plausible alternative to explicit regularization in CNNs. link
May 2018. Group of Multimedia Processing, University Carlos III of Madrid, Madrid (Spain). Data augmentation instead of explicit regularization.
December 2017. Deep Learning Meetup, Berlin (Germany). Data augmentation instead of explicit regularization.