About: Evolving Long Short-Term Memory Networks

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Evolving Long Short-Term Memory Networks
Creator	Coelho, Vicente Neto, Lobo Papa, João Passos, Leandro
topic	covid:d99df666aaa3f72cfc5468c1089b90bb29e29c69#this
Source	PMC
abstract	Machine learning techniques have been massively employed in the last years over a wide variety of applications, especially those based on deep learning, which obtained state-of-the-art results in several research fields. Despite the success, such techniques still suffer from some shortcomings, such as the sensitivity to their hyperparameters, whose proper selection is context-dependent, i.e., the model may perform better over each dataset when using a specific set of hyperparameters. Therefore, we propose an approach based on evolutionary optimization techniques for fine-tuning Long Short-Term Memory networks. Experiments were conducted over three public word-processing datasets for part-of-speech tagging. The results showed the robustness of the proposed approach for the aforementioned task.
has issue date	2020-06-15(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50417-5_25
has license	no-cc
sha1sum (hex)	d99df666aaa3f72cfc5468c1089b90bb29e29c69
schema:url	https://doi.org/10.1007/978-3-030-50417-5_25
resource representing a document's title	Evolving Long Short-Term Memory Networks
has PubMed Central identifier	PMC7302852
schema:publication	Computational Science - ICCS 2020
resource representing a document's body	covid:d99df666aaa3f72cfc5468c1089b90bb29e29c69#body_text
is http://vocab.deri.ie/void#inDataset of	proxy:http/ns.inria.fr/covid19/d99df666aaa3f72cfc5468c1089b90bb29e29c69 https://covidontheweb.inria.fr:4443/about/id/http/ns.inria.fr/covid19/d99df666aaa3f72cfc5468c1089b90bb29e29c69
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