%0 Journal Article %1 journals/nn/LambrechtsGVED23 %A Lambrechts, Gaspard %A Geeter, Florent De %A Vecoven, Nicolas %A Ernst, Damien %A Drion, Guillaume %D 2023 %J Neural Networks %K dblp %P 645-669 %T Warming up recurrent neural networks to maximise reachable multistability greatly improves learning. %U http://dblp.uni-trier.de/db/journals/nn/nn166.html#LambrechtsGVED23 %V 166

@article{journals/nn/LambrechtsGVED23, added-at = {2023-09-19T00:00:00.000+0200}, author = {Lambrechts, Gaspard and Geeter, Florent De and Vecoven, Nicolas and Ernst, Damien and Drion, Guillaume}, biburl = {https://www.bibsonomy.org/bibtex/25f8dc776e945d3da8e7d5281f0ebdf57/dblp}, ee = {https://doi.org/10.1016/j.neunet.2023.07.023}, interhash = {3d33c93bfd2138604b3369c02fd0c8e3}, intrahash = {5f8dc776e945d3da8e7d5281f0ebdf57}, journal = {Neural Networks}, keywords = {dblp}, month = {September}, pages = {645-669}, timestamp = {2024-04-09T05:23:54.000+0200}, title = {Warming up recurrent neural networks to maximise reachable multistability greatly improves learning.}, url = {http://dblp.uni-trier.de/db/journals/nn/nn166.html#LambrechtsGVED23}, volume = 166, year = 2023 }