27 citations · 61 across the 10 of their papers we have counts for
10 papers
Deep and Sparse Denoising Benchmarks for Spectral Data Cubes of High-z Galaxies: From Simulations to ALMA observations
Arnab Lahiry, Tanio Díaz-Santos, Jean-Luc Starck +4
Beyond cosmic noon, galaxies appear as faint whispers amid noise, yet this epoch is key to understanding massive galaxy assembly. ALMA's sensitivity to cold dust and [C II] emissio…
The More, the Merrier: Contrastive Fusion for Higher-Order Multimodal Alignment
Stefanos Koutoupis, Michaela Areti Zervou, Konstantinos Kontras +3
Learning joint representations across multiple modalities remains a central challenge in multimodal machine learning. Prevailing approaches predominantly operate in pairwise settin…
Measuring the Substructure Mass Power Spectrum of 23 SLACS Strong Galaxy-Galaxy Lenses with Convolutional Neural Networks
Joshua Fagin, Georgios Vernardos, Grigorios Tsagkatakis +3
Strong gravitational lensing can be used as a tool for constraining the substructure in the mass distribution of galaxies. In this study we investigate the power spectrum of dark m…
Quantifying the structure of strong gravitational lens potentials with uncertainty-aware deep neural networks
Georgios Vernardos, Grigorios Tsagkatakis, Yannis Pantazis
Gravitational lensing is a powerful tool for constraining substructure in the mass distribution of galaxies, be it from the presence of dark matter sub-halos or due to physical mec…
Adversarial dictionary learning for a robust analysis and modelling of spontaneous neuronal activity
Eirini Troullinou, Grigorios Tsagkatakis, Ganna Palagina +3
The field of neuroscience is experiencing rapid growth in the complexity and quantity of the recorded neural activity allowing us unprecedented access to its dynamics in different…
Artificial neural networks in action for an automated cell-type classification of biological neural networks
Eirini Troullinou, Grigorios Tsagkatakis, Spyridon Chavlis +5
Identification of different neuronal cell types is critical for understanding their contribution to brain functions. Yet, automated and reliable classification of neurons remains a…
Quasar microlensing light curve analysis using deep machine learning
Georgios Vernardos, Grigorios Tsagkatakis
We introduce a deep machine learning approach to studying quasar microlensing light curves for the first time by analyzing hundreds of thousands of simulated light curves with resp…
A Distributed Learning Architecture for Scientific Imaging Problems
A. Panousopoulou, S. Farrens, K. Fotiadou +4
Current trends in scientific imaging are challenged by the emerging need of integrating sophisticated machine learning with Big Data analytics platforms. This work proposes an in-m…
Convolutional Neural Networks for Video Quality Assessment
Michalis Giannopoulos, Grigorios Tsagkatakis, Saverio Blasi +5
Video Quality Assessment (VQA) is a very challenging task due to its highly subjective nature. Moreover, many factors influence VQA. Compression of video content, while necessary f…
Convolutional Neural Networks for Spectroscopic Redshift Estimation on Euclid Data
Radamanthys Stivaktakis, Grigorios Tsagkatakis, Bruno Moraes +3
In this paper, we address the problem of spectroscopic redshift estimation in Astronomy. Due to the expansion of the Universe, galaxies recede from each other on average. This move…