no code implementations • 2 Jul 2023 • Zsolt Zombori, Agapi Rissaki, Kristóf Szabó, Wolfgang Gatterbauer, Michael Benedikt
We consider learning a probabilistic classifier from partially-labelled supervision (inputs denoted with multiple possibilities) using standard neural architectures with a softmax as the final layer.
no code implementations • 9 Dec 2020 • Agapi Rissaki, Orestis Pavlou, Dimitris Fotakis, Vicky Papadopoulou, Andreas Efstathiou
We propose an end-to-end approach for solving inverse problems for a class of complex astronomical signals, namely Spectral Energy Distributions (SEDs).