Results 91 to 100 of about 99,773 (316)
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 +5 more
openaire +2 more sources
Design of exponential state estimators for neural networks with mixed time delays
, 2007 This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Elsevier Ltd.In this Letter, the state estimation problem is dealt with for a class of recurrent neural networks (RNNs ...
Liu, Y, Liu, X, Wang, Z
core +1 more source
Recurrent Neural Networks for Temporal Data Processing [PDF]
, 2011 The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the connections are not feed-forward ones only. In RNNs, connections between units form directed cycles, providing an implicit internal memory.
core +1 more source
Analyzing Echo-state Networks Using Fractal Dimension
, 2022 This work joins aspects of reservoir optimization, information-theoretic optimal encoding, and at its center fractal analysis. We build on the observation that, due to the recursive nature of recurrent neural networks, input sequences appear as fractal ...
Obst, Oliver +3 more
core +1 more source
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 +8 more
wiley +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 +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
, 2009 Copyright [2009] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services.
Liu, Y +11 more
core +1 more source
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
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 +3 more
openaire +3 more sources