Results 41 to 50 of about 296,489 (278)
, 2018 Recurrent neural networks can learn complex transduction problems that require maintaining and actively exploiting a memory of their inputs. Such models traditionally consider memory and input-output functionalities indissolubly entangled. We introduce a
Bacciu, Davide +2 more
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Multi-step learning rule for recurrent neural models: an application to time series forecasting [PDF]
, 2001 Multi-step prediction is a difficult task that has attracted increasing interest in recent years. It tries to achieve predictions several steps ahead into the future starting from current information.
Galván, Inés M., Isasi, Pedro
core +2 more sources
Learning extreme vegetation response to climate drivers with recurrent neural networks [PDF]
Nonlinear Processes in GeophysicsThe spectral signatures of vegetation are indicative of ecosystem states and health. Spectral indices used to monitor vegetation are characterized by long-term trends, seasonal fluctuations, and responses to weather anomalies. This study investigates the
F. Martinuzzi +13 more
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Network of Recurrent Neural Networks
CoRR, 2017 Under review as a conference paper at AAAI ...
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Relational recurrent neural networks
CoRR, 2018 Memory-based neural networks model temporal data by leveraging an ability to remember information for long periods. It is unclear, however, whether they also have an ability to perform complex relational reasoning with the information they remember.
Adam Santoro +9 more
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Neural Approximators for Variable-Order Fractional Calculus Operators (VO-FC)
IEEE Access, 2022 The paper presents research on the approximation of variable-order fractional operators by recurrent neural networks. The research focuses on two basic variable-order fractional operators, i.e., integrator and differentiator.
Bartosz Puchalski
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Dilated Recurrent Neural Networks
CoRR, 2017 Learning with recurrent neural networks (RNNs) on long sequences is a notoriously difficult task. There are three major challenges: 1) complex dependencies, 2) vanishing and exploding gradients, and 3) efficient parallelization. In this paper, we introduce a simple yet effective RNN connection structure, the DilatedRNN, which simultaneously tackles all
Shiyu Chang +9 more
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NLOS Identification in WLANs Using Deep LSTM with CNN Features
Sensors, 2018 Identifying channel states as line-of-sight or non-line-of-sight helps to optimize location-based services in wireless communications. The received signal strength identification and channel state information are used to estimate channel conditions for ...
Viet-Hung Nguyen +3 more
doaj +1 more source
Video Description using Bidirectional Recurrent Neural Networks
, 2016 Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions.
Bolaños, Marc +3 more
core +1 more source
Clinical Insights Into Hypercalcemia of Malignancy in Childhood
Pediatric Blood &Cancer, EarlyView.ABSTRACT Hypercalcemia of malignancy (HCM) is a rare but life‐threatening metabolic emergency in children that occurs in less than 1% of pediatric cancer cases, with a reported incidence ranging from 0.4% to 1.0% across different studies. While it is observed in 10%–20% of adult malignancies, pediatric HCM remains relatively uncommon.
Hüseyin Anıl Korkmaz
wiley +1 more source