Results 71 to 80 of about 37,616 (181)

Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

, 2018
12 pages, 21 figures, 3 ...
Alom, Md Zahangir, Hasan, Mahmudul, Yakopcic, Chris, Taha, Tarek M., Asari, Vijayan K.   +4 more
openaire   +2 more sources

Overcoming the Vanishing Gradient Problem during Learning Recurrent Neural Nets (RNN)

Asian Journal of Applied Science and Engineering, 2020
Artificial neural nets have been equipped with working out the difficulty that arises as a result of exploding and vanishing gradients. The difficulty of working out is worsened exponentially particularly in deep learning understanding. With gradient-oriented learning approaches the up-to-date error gesture has to “flow back in time” throughout the ...
openaire   +1 more source

Auxiliaries’ power and energy demand prediction of battery electric vehicles using system identification and deep learning

IET Intelligent Transport Systems
The energy demand of the auxiliaries of battery electric vehicles can account for a significant share of the total energy demand of a trip and must be taken into account for the prediction of the vehicle's remaining driving range or the implementation of
Lukas Schäfers, Kai Franke, Rene Savelsberg, Stefan Pischinger   +3 more
doaj   +1 more source

RHR-Net: A Residual Hourglass Recurrent Neural Network for Speech Enhancement

, 2019
Most current speech enhancement models use spectrogram features that require an expensive transformation and result in phase information loss. Previous work has overcome these issues by using convolutional networks to learn long-range temporal correlations across high-resolution waveforms.
Abdulbaqi, Jalal, Gu, Yue, Marsic, Ivan
openaire   +2 more sources

Programming Patterns in Dataflow Matrix Machines and Generalized Recurrent Neural Nets

, 2016
13 pages (v2 - update references)
Bukatin, Michael, Matthews, Steve, Radul, Andrey   +2 more
openaire   +2 more sources

A predictive SD‐WAN traffic management method for IoT networks in multi‐datacenters using deep RNN

IET Communications
Deploying the Internet of Things (IoT) in integrated edge‐cloud environments exposes the IoT traffic data to performance issues such as delay, bandwidth limitation etc.
Zeinab Nazemi Absardi, Reza Javidan
doaj   +1 more source

Toward AI-Augmented Formal Verification: A Preliminary Investigation of ENGRU and Its Challenges

IEEE Access
State-space graphs and automata serve as fundamental tools for modeling and analyzing the behavior of computational systems. Recurrent neural networks (RNNs) and language models are deeply intertwined, as RNNS provide the foundational architecture that ...
Chanon Dechsupa, Teerapong Panboonyuen, Wiwat Vatanawood, Praisan Padungweang, Chakchai So-In   +4 more
doaj   +1 more source

Global Attention‐Based Dynamic Multi‐Graph Convolutional Recurrent Network for Traffic Flow Forecasting

IET Intelligent Transport Systems
Accurate traffic flow forecasting is a challenging task in intelligent transportation system. With traffic flow forecasting being formulated as a spatio‐temporal graph modelling problem, graph convolution network (GCN) is increasingly used in recent ...
Jinfeng Hou, Shouwen Ji, Lei Chen, Dong Guo   +3 more
doaj   +1 more source

Talk 2: Recurrent neural nets and differentiable memory mechanism

, 2016
This past year, RNNs have seen a lot of attention as powerful models that are able to decode sequences from signals. The key component of such methods are the use of a recurrent neural network architecture that is trained end-to-end to optimize the probability of the output sequence given those signals.
openaire   +1 more source

Dense and distributed neuropeptide network in the nerve net of Hydra vulgaris. [PDF]

PLoS Comput Biol
De La Cruz Rothenfusser J, Yáñez-Guerra LA, Teufel F, Yuste R.   +3 more
europepmc   +1 more source