Results 91 to 100 of about 7,083,147 (335)
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
openaire +4 more sources
Lipschitz Recurrent Neural Networks
CoRR, 2020 Published as a conference paper at ICLR ...
N. Benjamin Erichson +4 more
openaire +3 more sources
Molecular Oncology, EarlyView.Dormant cancer cells can hide in distant organs for years, evading treatment and the immune system. This review highlights how signals from the surrounding tissue and immune environment keep these cells inactive or trigger their reawakening. Understanding these mechanisms may help develop therapies to eliminate or control dormant cells and prevent ...
Kanishka Tiwary +1 more
wiley +1 more source
Micrometer Backstepping Control System for Linear Motion Single Axis Robot Machine Drive
Sensors, 2019 In order to cut down influence on the uncertainty disturbances of a linear motion single axis robot machine, such as the external load force, the cogging force, the column friction force, the Stribeck force, and the parameters variations, the micrometer ...
Chih-Hong Lin, Kuo-Tsai Chang
doaj +1 more source
Linked Recurrent Neural Networks
CoRR, 2018 Recurrent Neural Networks (RNNs) have been proven to be effective in modeling sequential data and they have been applied to boost a variety of tasks such as document classification, speech recognition and machine translation. Most of existing RNN models have been designed for sequences assumed to be identically and independently distributed (i.i.d ...
Zhiwei Wang 0001 +3 more
openaire +3 more sources
Stochastic graph recurrent neural network
Neurocomputing, 2022 Representation learning over graph structure data has been widely studied due to its wide application prospects. However, previous methods mainly focus on static graphs while many real-world graphs evolve over time. Modeling such evolution is important for predicting properties of unseen networks.
Tijin Yan +3 more
openaire +4 more sources
Molecular Oncology, EarlyView.Single‐cell multi‐omics reveals epigenetic heterogeneity across therapy‐adaptive tumor states, including quiescent/dormant, drug‐tolerant persister, and EMT‐like phenotypes. By linking regulatory features with state‐associated biomarkers, these approaches inform biomarker‐guided therapeutic strategies for evolving tumors.
Hee Jung Kim +3 more
wiley +1 more source
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 +3 more
doaj +2 more sources
Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting. [PDF]
PLoS ONE, 2016 Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting.
Waddah Waheeb +2 more
doaj +1 more source