no code implementations • 31 May 2023 • Robert-Jan Bruintjes, Attila Lengyel, Marcos Baptista Rios, Osman Semih Kayhan, Davide Zambrano, Nergis Tomen, Jan van Gemert
The third edition of the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" workshop featured four data-impaired challenges, focusing on addressing the limitations of data availability in training deep learning models for computer vision tasks.
4 code implementations • 17 Aug 2022 • Gabriel Van Zandycke, Vladimir Somers, Maxime Istasse, Carlo Del Don, Davide Zambrano
With the recent development of Deep Learning applied to Computer Vision, sport video understanding has gained a lot of attention, providing much richer information for both sport consumers and leagues.
no code implementations • 21 Jan 2022 • Attila Lengyel, Robert-Jan Bruintjes, Marcos Baptista Rios, Osman Semih Kayhan, Davide Zambrano, Nergis Tomen, Jan van Gemert
The second edition of the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" challenges featured five data-impaired challenges, where models are trained from scratch on a reduced number of training samples for various key computer vision tasks.
1 code implementation • 25 Apr 2021 • Enrico Zardini, Davide Zappetti, Davide Zambrano, Giovanni Iacca, Dario Floreano
Designing optimal soft modular robots is difficult, due to non-trivial interactions between morphology and controller.
1 code implementation • ICLR 2018 • Bojian Yin, Marleen Balvert, Davide Zambrano, Alexander Schönhuth, Sander Bohte
The folding structure of the DNA molecule combined with helper molecules, also referred to as the chromatin, is highly relevant for the functional properties of DNA.
no code implementations • ICLR 2018 • Davide Zambrano, Isabella Pozzi, Roeland Nusselder, Sander Bohte
These adaptive spiking neurons implement an adaptive form of sigma-delta coding to convert internally computed analog activation values to spike-trains.
no code implementations • 13 Oct 2017 • Davide Zambrano, Roeland Nusselder, H. Steven Scholte, Sander Bohte
Adaptive spike-time coding additionally allows for the dynamic control of neural coding precision: we show how a simple model of arousal in AdSNNs further halves the average required firing rate and this notion naturally extends to other forms of attention.
no code implementations • 7 Sep 2016 • Davide Zambrano, Sander M. Bohte
It is an open question how real spiking neurons produce the kind of powerful neural computation that is possible with deep artificial neural networks, using only so very few spikes to communicate.