Publications | Georgios Exarchakishttps://exarchakis.net/publication/PublicationsWowchemy (https://wowchemy.com)en-us© 2026https://exarchakis.net/media/icon_hua2ec155b4296a9c9791d015323e16eb5_11927_512x512_fill_lanczos_center_2.pngPublicationshttps://exarchakis.net/publication/Predicting COVID-19 positivity and hospitalization with multi-scale graph neural networkshttps://exarchakis.net/publication/predicting-covid-19-positivity-and-hospitalization-with-multi-scale-graph-neural-networks/Sat, 21 Mar 2026 17:08:43 +0000https://exarchakis.net/publication/predicting-covid-19-positivity-and-hospitalization-with-multi-scale-graph-neural-networks/Prediction of cesarean delivery in class III obese nulliparous women: An externally validated model using machine learninghttps://exarchakis.net/publication/prediction-of-cesarean-delivery-in-class-iii-obese-nulliparous-women-an-externally-validated-model-using-machine-learning/Sat, 21 Mar 2026 17:07:00 +0000https://exarchakis.net/publication/prediction-of-cesarean-delivery-in-class-iii-obese-nulliparous-women-an-externally-validated-model-using-machine-learning/Solid Harmonic Wavelet Bispectrum for Image Analysishttps://exarchakis.net/publication/solid-harmonic-wavelet-bispectrum-for-image-analysis/Wed, 03 Dec 2025 17:14:00 +0000https://exarchakis.net/publication/solid-harmonic-wavelet-bispectrum-for-image-analysis/Efficient spatio-temporal feature clustering for large event-based datasetshttps://exarchakis.net/publication/efficient-spatio-temporal-feature-clustering-for-large-event-based-datasets/Tue, 04 Oct 2022 14:57:50 +0000https://exarchakis.net/publication/efficient-spatio-temporal-feature-clustering-for-large-event-based-datasets/Dissecting Self-Supervised Learning Methods for Surgical Computer Visionhttps://exarchakis.net/publication/dissecting-self-supervised-learning-methods-for-surgical-computer-vision/Tue, 05 Jul 2022 08:59:06 +0000https://exarchakis.net/publication/dissecting-self-supervised-learning-methods-for-surgical-computer-vision/A sampling-based approach for efficient clustering in large datasetshttps://exarchakis.net/publication/a-sampling-based-approach-for-efficient-clustering-in-large-datasets/Thu, 05 May 2022 08:59:06 +0000https://exarchakis.net/publication/a-sampling-based-approach-for-efficient-clustering-in-large-datasets/ProSper - A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositionshttps://exarchakis.net/publication/prosper/Mon, 19 Aug 2019 13:48:09 +0300https://exarchakis.net/publication/prosper/Kymatio: Scattering Transforms in Pythonhttps://exarchakis.net/publication/kymatio-scattering-transforms-in-python/Fri, 28 Dec 2018 00:00:00 +0000https://exarchakis.net/publication/kymatio-scattering-transforms-in-python/<!-- More detail can easily be written here using *Markdown* and $\rm \LaTeX$ math code. -->Truncated Variational Sampling for ‘Black Box’ Optimization of Generative Modelshttps://exarchakis.net/publication/truncated-variational-sampling-for-black-box-optimization-of-generative-models/Sat, 30 Jun 2018 14:38:58 +0200https://exarchakis.net/publication/truncated-variational-sampling-for-black-box-optimization-of-generative-models/Solid harmonic wavelet scattering for predictions of molecule propertieshttps://exarchakis.net/publication/solid-harmonic-wavelet-scattering-for-predictions-of-molecule-properties/Thu, 28 Jun 2018 00:00:00 +0000https://exarchakis.net/publication/solid-harmonic-wavelet-scattering-for-predictions-of-molecule-properties/<!-- # More detail can easily be written here using *Markdown* and $\rm \LaTeX$ math code. -->Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densitieshttps://exarchakis.net/publication/solid-harmonic-wavelet-scattering-predicting-quantum-molecular-energy-from-invariant-descriptors-of-3d-electronic-densities/Tue, 21 Nov 2017 00:00:00 +0000https://exarchakis.net/publication/solid-harmonic-wavelet-scattering-predicting-quantum-molecular-energy-from-invariant-descriptors-of-3d-electronic-densities/<!-- # More detail can easily be written here using *Markdown* and $\rm \LaTeX$ math code. -->Discrete Sparse Codinghttps://exarchakis.net/publication/discrete-sparse-coding/Wed, 01 Nov 2017 00:00:00 +0000https://exarchakis.net/publication/discrete-sparse-coding/<!-- More detail can easily be written here using *Markdown* and $\rm \LaTeX$ math code. -->Probabilistic Models for Invariant Representations and Transformationshttps://exarchakis.net/publication/probabilistic-models-for-invariant-representations-and-transformations/Thu, 01 Dec 2016 00:00:00 +0000https://exarchakis.net/publication/probabilistic-models-for-invariant-representations-and-transformations/<!-- More detail can easily be written here using *Markdown* and $\rm \LaTeX$ math code. -->What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approachhttps://exarchakis.net/publication/what-are-the-invariant-occlusive-components-of-image-patches-a-probabilistic-generative-approach/Thu, 05 Dec 2013 00:00:00 +0000https://exarchakis.net/publication/what-are-the-invariant-occlusive-components-of-image-patches-a-probabilistic-generative-approach/<!-- More detail can easily be written here using *Markdown* and $\rm \LaTeX$ math code. -->Learning invariant features by harnessing the aperture problemhttps://exarchakis.net/publication/learning-invariant-features-by-harnessing-the-aperture-problem/Wed, 05 Jun 2013 00:00:00 +0000https://exarchakis.net/publication/learning-invariant-features-by-harnessing-the-aperture-problem/<!-- More detail can easily be written here using *Markdown* and $\rm \LaTeX$ math code. -->Ternary Sparse Codinghttps://exarchakis.net/publication/ternary-sparse-coding/Mon, 18 Jun 2012 18:29:37 +0200https://exarchakis.net/publication/ternary-sparse-coding/Discrete Symmetric Priors for Sparse Codinghttps://exarchakis.net/publication/discrete-symmetric-priors-for-sparse-coding/Tue, 04 Oct 2011 00:00:00 +0000https://exarchakis.net/publication/discrete-symmetric-priors-for-sparse-coding/<!-- More detail can easily be written here using *Markdown* and $\rm \LaTeX$ math code. -->