1 code implementation • 6 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.
1 code implementation • 4 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.
1 code implementation • 12 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.
1 code implementation • 26 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.
1 code implementation • 6 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.
1 code implementation • article belongs to the Special Issue Applications of Deep Learning in Smart Agriculture 2021 • François Waldner, Foivos I. Diakogiannis, Kathryn Batchelor, Michael Ciccotosto-Camp, Elizabeth Cooper-Williams, Chris Herrmann, Gonzalo Mata, Andrew Toovey 7
Thus, knowing the exact location of fields and their boundaries is a prerequisite.
1 code implementation • 4 Sep 2020 • Foivos I. Diakogiannis, François Waldner, Peter Caccetta
Further, we introduce a new encoder/decoder scheme, a network macro-topology, that is tailored for the task of change detection.
Building change detection for remote sensing images Change Detection +1
no code implementations • 26 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.
7 code implementations • 1 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.