Publications

Computational methods for continuous eye-tracking perimetry based on spatio-temporal integration and a deep recurrent neural network. Alessandro Grillini, Alex Hernandez-Garcia, Remco J. Renken, Giorgia Demaria, Frans W. Cornelissen, 2021. Frontiers in Neuroscience. Frontiers in Neuroscience

Benchmark of data processing methods and machine learning models for gut microbiome-based diagnosis of inflammatory bowel disease. Ryszard Kubinski, Jean-Yves Djamen-Kepaou, Timur Zhanabaev, Alex Hernandez-Garcia, Stefan Bauer, Falk Hildebrand, Tamas Korcsmaros, Sani Karam, Prevost Jantchou, Kamran Kafi, Ryan D. Martin, 2021. bioRxiv. bioRxiv

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 arXiv

A machine learning pipeline to predict vegetation health. Thomas Lees, Gabriel Tseng, Alex Hernandez-Garcia, Clement Atzberger, Simon Dadson, Steven Reece, 2020. International Conference on Learning Representations (ICLR), Workshop on Tackling climate change with machine learning. Spotlight PDF code

Learning representational invariance instead of categorization. Alex Hernandez-Garcia, Peter König, 2019. International Conference on Computer Vision (ICCV), Workshop on pre-registration in Computer Vision. PDF code

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

Data augmentation instead of explicit regularization. Alex Hernandez-Garcia, Peter König, 2018. arXiv:1806.03852. arXiv code

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

Do deep nets really need weight decay and dropout?. Alex Hernandez-Garcia, Peter König, 2018. arXiv:1802.07042. arXiv code

Further advantages of data augmentation on convolutional neural networks. Alex Hernandez-Garcia, Peter König, 2018. International Conference on Artificial Neural Networks (ICANN). Best Paper Award arXiv code

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

December 2020. AFC Lab Talk Series, hosted by Meike Ramon, Online (The Internet). Global visual salience of competing stimuli. link video

November 2020. Brain & Cognitive Society, IIT Kanpur, KanpurOnline (IndiaThe Internet). More than more: Insights from data augmentation for deep learning and computational neuroscience. link video

November 2020. Machine Learning Research Group, University of Guelph, GuelphOnline (Ontario, CanadaThe Internet). Data augmentation and image understanding.

October 2020. neuromatch 2020 3.0, Online (The Internet). Global visual salience of competing stimuli. link video

June 2020. Berlin Machine Learning Seminar, BerlinOnline (GermanyThe Internet). Learning robust visual representations using data augmentation invariance. link

May 2020. UNIQUE Student Symposium, MontrealOnline (Quebec, CanadaThe Internet). Learning robust visual representations using data augmentation invariance. link video

April 2020. International Conference on Learning Representations (ICLR), Workshop on Bridging AI and Cognitve Science (BAICS), Addis AbabaOnline (EthiopiaThe Internet). Learning robust visual representations using data augmentation invariance. link video

March 2020. neuromatch 2020 1.0, Online (The Internet). Data augmentation invariance for learning robust visual representations. link

November 2019. International Conference on Computer Vision (ICCV), Workshop on pre-registration in Computer Vision, Seoul (Republic of Korea). Learning representational invariance instead of categorization. link video

November 2019. Department of Brain and Cognitive Engineering, Korea University, Seoul (Republic of Korea). Data augmentation for improved regularization and invariance learning.

October 2019. Computational Cognition, Osnabrück (Germany). Learning robust visual representations using data augmentation invariance. link video

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.