Results 81 to 90 of about 92,175 (266)

Memristive recurrent neural network

Neurocomputing, 2018
Abstract It is reported a continuous-time neural network in CMOS that uses memristors. These nanodevices are used to achieve some analog functions such as constant current sourcing, decaying term emulation, and resistive connection; all of them representing parameters of the neural network.
Gerardo Marcos Tornez-Xavier, Felipe Gomez-Castañeda, Luis M. Flores-Nava, Jose Antonio Moreno-Cadenas   +3 more
openaire   +1 more source

Fusion Recurrent Neural Network

CoRR, 2020
Considering deep sequence learning for practical application, two representative RNNs - LSTM and GRU may come to mind first. Nevertheless, is there no chance for other RNNs? Will there be a better RNN in the future? In this work, we propose a novel, succinct and promising RNN - Fusion Recurrent Neural Network (Fusion RNN).
Yiwen Sun, Yulu Wang, Kun Fu 0002, Zheng Wang 0010, Changshui Zhang, Jieping Ye   +5 more
openaire   +2 more sources

Somatic mutational landscape in von Hippel–Lindau familial hemangioblastoma

Molecular Oncology, EarlyView.
The causes of central nervous system (CNS) hemangioblastoma in Von Hippel–Lindau (vHL) disease are unclear. We used Whole Exome Sequencing (WES) on familial hemangioblastoma to investigate events that underlie tumor development. Our findings suggest that VHL loss creates a permissive environment for tumor formation, while additional alterations ...
Maja Dembic, Anne Nørremølle, Lilian Bomme Ousager, Lars van Brakel Andersen, Marie Louise Mølgaard Binderup, Mads Thomassen   +5 more
wiley   +1 more source

Study of emotion recognition based on fusion multi-modal bio-signal with SAE and LSTM recurrent neural network

Tongxin xuebao, 2017
In order to achieve more accurate emotion recognition accuracy from multi-modal bio-signal features，a novel method to extract and fuse the signal with the stacked auto-encoder and LSTM recurrent neural networks was proposed.The stacked auto-encoder ...
You-jun LI, Jia-jin HUANG, Hai-yuan WANG, Ning ZHONG   +3 more
doaj   +2 more sources

Signal demodulator based on in‐phase and quadrature interference‐robust feature

Electronics Letters, 2023
This letter examines the issue of mitigating strong co‐channel interference in communication systems is addressed. Unlike conventional model‐based methods, a novel data‐driven scheme is proposed.
Wen Deng, Xin Cai, Xiang Wang, Zhitao Huang   +3 more
doaj   +1 more source

Reversible Recurrent Neural Networks

CoRR, 2018
Recurrent neural networks (RNNs) provide state-of-the-art performance in processing sequential data but are memory intensive to train, limiting the flexibility of RNN models which can be trained. Reversible RNNs---RNNs for which the hidden-to-hidden transition can be reversed---offer a path to reduce the memory requirements of training, as hidden ...
Matthew MacKay, Paul Vicol, Jimmy Ba, Roger B. Grosse   +3 more
openaire   +3 more sources

Temporal-Kernel Recurrent Neural Networks [PDF]

Neural Networks, 2010
A Recurrent Neural Network (RNN) is a powerful connectionist model that can be applied to many challenging sequential problems, including problems that naturally arise in language and speech. However, RNNs are extremely hard to train on problems that have long-term dependencies, where it is necessary to remember events for many timesteps before using ...
Ilya Sutskever, Geoffrey E. Hinton
openaire   +2 more sources

CD47 promotes mitogen‐activated protein kinase and epithelial‐to‐mesenchymal transition molecular programs to drive prometastatic phenotypes in non‐small cell lung cancer

Molecular Oncology, EarlyView.
Beyond its role in immune evasion, this study identified that CD47 drives tumor‐intrinsic signaling in non‐small cell lung cancer (NSCLC). Transcriptomic profiling and functional studies revealed that CD47 regulates cell adhesion, migration, and metastasis through an ERK–EMT signaling axis.
Asa P.Y. Lau, Lilian G. Zhai, Ryunosuke Hoshi, Zackary Rousseau, Abraam Zakhary, Yin Fang Wu, Rola M. Saleeb, Heyu Ni, Kelsie L. Thu   +8 more
wiley   +1 more source

Artificial Intelligence Image Recognition Method Based on Convolutional Neural Network Algorithm

IEEE Access, 2020
As an algorithm with excellent performance, convolutional neural network has been widely used in the field of image processing and achieved good results by relying on its own local receptive fields, weight sharing, pooling, and sparse connections.
Youhui Tian
doaj   +1 more source

Temporal overdrive recurrent neural network [PDF]

2017 International Joint Conference on Neural Networks (IJCNN), 2017
In this work we present a novel recurrent neural network architecture designed to model systems characterized by multiple characteristic timescales in their dynamics. The proposed network is composed by several recurrent groups of neurons that are trained to separately adapt to each timescale, in order to improve the system identification process.
Filippo Maria Bianchi, Michael Kampffmeyer, Enrico Maiorino, Robert Jenssen   +3 more
openaire   +3 more sources