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De Geeter Florent

Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation

Montefiore Institute

See author's contact details
ORCID
0009-0000-0977-7244
Main Referenced Co-authors
Drion, Guillaume  (2)
Ernst, Damien  (2)
Lambrechts, Gaspard  (1)
Vecoven, Nicolas (1)
Main Referenced Keywords
backpropagation (1); deep learning (1); Initialisation procedure (1); Long time dependencies (1); Long-term memory (1);
Main Referenced Disciplines
Computer science (2)

Publications (total 2)

The most downloaded
185 downloads
Lambrechts, G.* , De Geeter, F.* , Vecoven, N.* , Ernst, D., & Drion, G. (August 2023). Warming up recurrent neural networks to maximise reachable multistability greatly improves learning. Neural Networks, 166, 645-669. doi:10.1016/j.neunet.2023.07.023 https://hdl.handle.net/2268/260699

The most cited

1 citations (Scopus®)

De Geeter, F., Ernst, D., & Drion, G. (15 May 2024). Spike-based computation using classical recurrent neural networks. Neuromorphic Computing and Engineering, 4 (2), 024007. doi:10.1088/2634-4386/ad473b https://hdl.handle.net/2268/303622

De Geeter, F., Ernst, D., & Drion, G. (15 May 2024). Spike-based computation using classical recurrent neural networks. Neuromorphic Computing and Engineering, 4 (2), 024007. doi:10.1088/2634-4386/ad473b
Peer Reviewed verified by ORBi

Lambrechts, G.* , De Geeter, F.* , Vecoven, N.* , Ernst, D., & Drion, G. (August 2023). Warming up recurrent neural networks to maximise reachable multistability greatly improves learning. Neural Networks, 166, 645-669. doi:10.1016/j.neunet.2023.07.023
Peer Reviewed verified by ORBi
* These authors have contributed equally to this work.

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