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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
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Temporal codes and recurrent timing nets for rhythmic expectancy [PDF]
Frontiers in Computational NeuroscienceThis paper focuses on possible time-domain neurocomputational mechanisms for short-term anticipatory processes. Here we present a simple, signal processing functional model of how short-term rhythmic pattern expectancies could be computed on the fly ...
Peter Cariani +2 more
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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
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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
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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
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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
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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
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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
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Enhanced road information representation in graph recurrent network for traffic speed prediction
IET Intelligent Transport Systems, 2023 Correctly capturing the spatial‐temporal correlation of traffic sequences will benefit to make accurate predictions of the future traffic states. In the paper, the methods of enhancing road spatial and temporal information representation are proposed ...
Lei Chang +4 more
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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
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