Search Results for author: Foivos I. Diakogiannis

Found 9 papers, 8 papers with code

Physical Symbolic Optimization

1 code implementation6 Dec 2023 Wassim Tenachi, Rodrigo Ibata, Foivos I. Diakogiannis

We present a framework for constraining the automatic sequential generation of equations to obey the rules of dimensional analysis by construction.

regression reinforcement-learning +1

Class Symbolic Regression: Gotta Fit 'Em All

1 code implementation4 Dec 2023 Wassim Tenachi, Rodrigo Ibata, Thibaut L. François, Foivos I. Diakogiannis

We introduce "Class Symbolic Regression" a first framework for automatically finding a single analytical functional form that accurately fits multiple datasets - each governed by its own (possibly) unique set of fitting parameters.

regression Symbolic Regression

SSG2: A new modelling paradigm for semantic segmentation

1 code implementation12 Oct 2023 Foivos I. Diakogiannis, Suzanne Furby, Peter Caccetta, Xiaoliang Wu, Rodrigo Ibata, Ondrej Hlinka, John Taylor

By adding this "temporal" dimension, we exploit strong signal correlations between successive observations in the sequence to reduce error rates.

Change Detection Lesion Segmentation +3

An end-to-end strategy for recovering a free-form potential from a snapshot of stellar coordinates

1 code implementation26 May 2023 Wassim Tenachi, Rodrigo Ibata, Foivos I. Diakogiannis

New large observational surveys such as Gaia are leading us into an era of data abundance, offering unprecedented opportunities to discover new physical laws through the power of machine learning.

Symbolic Regression

Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws

1 code implementation6 Mar 2023 Wassim Tenachi, Rodrigo Ibata, Foivos I. Diakogiannis

Here we present $\Phi$-SO, a Physical Symbolic Optimization framework for recovering analytical symbolic expressions from physics data using deep reinforcement learning techniques by learning units constraints.

regression Symbolic Regression

Deep learning on edge: extracting field boundaries from satellite images with a convolutional neural network

no code implementations26 Oct 2019 François Waldner, Foivos I. Diakogiannis

By minimising image preprocessing requirements and replacing local arbitrary decisions by data-driven ones, our approach is expected to facilitate the extraction of individual crop fields at scale.

Semantic Segmentation

ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data

7 code implementations1 Apr 2019 Foivos I. Diakogiannis, François Waldner, Peter Caccetta, Chen Wu

Scene understanding of high resolution aerial images is of great importance for the task of automated monitoring in various remote sensing applications.

Scene Understanding Segmentation +1

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