Results 1 to 10 of about 37,616 (181)
Modeling Interval Timing by Recurrent Neural Nets [PDF]
Frontiers in Integrative Neuroscience, 2019 The purpose of this study was to take a new approach in showing how the central nervous system might encode time at the supra-second level using recurrent neural nets (RNNs).
Theodore Raphan +5 more
doaj +5 more sources
Bayesian error propagation for neural-net based parameter inference
The Open Journal of Astrophysics, 2022 Neural nets have become popular to accelerate parameter inferences, especially for the upcoming generation of galaxy surveys in cosmology. As neural nets are approximative by nature, a recurrent question has been how to propagate the neural net's ...
Daniela Grandón, Elena Sellentin
doaj +1 more source
Sensors, 2021 Sea fog is a natural phenomenon that reduces the visibility of manned vehicles and vessels that rely on the visual interpretation of traffic. Fog clearance, also known as fog dissipation, is a relatively under-researched area when compared with fog ...
Jin Hyun Han +5 more
doaj +1 more source
Novel multi‐domain attention for abstractive summarisation
CAAI Transactions on Intelligence Technology, 2023 The existing abstractive text summarisation models only consider the word sequence correlations between the source document and the reference summary, and the summary generated by models lacks the cover of the subject of source document due to models ...
Chunxia Qu +4 more
doaj +1 more source
Mack-Net model: Blending Mack’s model with Recurrent Neural Networks
Expert Systems with Applications, 2022 In general insurance companies, a correct estimation of liabilities plays a key role due to its impact on management and investing decisions. Since the Financial Crisis of 2007-2008 and the strengthening of regulation, the focus is not only on the total reserve but also on its variability, which is an indicator of the risk assumed by the company. Thus,
Eduardo Ramos-Pérez +2 more
openaire +3 more sources
IET Generation, Transmission & Distribution, 2023 Numerous studies on short‐term load forecasting (STLF) have used feature extraction methods to increase the model's accuracy by incorporating multidimensional features containing time, weather and distance information.
Shiyan Yi +4 more
doaj +1 more source
Brain inspired neuronal silencing mechanism to enable reliable sequence identification
Scientific Reports, 2022 Real-time sequence identification is a core use-case of artificial neural networks (ANNs), ranging from recognizing temporal events to identifying verification codes.
Shiri Hodassman +7 more
doaj +1 more source
The Context-Dependent Additive Recurrent Neural Net [PDF]
Proceedings of the 2018 Conference of the North American Chapter of
the Association for Computational Linguistics: Human Language
Technologies, Volume 1 (Long Papers), 2018 Contextual sequence mapping is one of the fundamental problems in Natural Language Processing (NLP). Here, instead of relying solely on the information presented in the text, the learning agents have access to a strong external signal given to assist the learning process.
Quan Hung Tran +5 more
openaire +1 more source
A multivariate natural gas load forecasting method based on residual recurrent neural network
Electronics Letters, 2023 Current natural gas load forecasting encounters with the conundrum of unsatisfying accuracy and interpretability. To address the challenge, a multi‐variate forecasting method is proposed, which contains three phases: First, an integrate history‐climate ...
Xueqing Ni +3 more
doaj +1 more source
On Learning Interpreted Languages with Recurrent Models
Computational Linguistics, 2022 Can recurrent neural nets, inspired by human sequential data processing, learn to understand language? We construct simplified data sets reflecting core properties of natural language as modeled in formal syntax and semantics: recursive syntactic ...
Denis Paperno
doaj +1 more source