Results 101 to 110 of about 99,773 (316)
Adaptive spatiotemporal neural networks through complementary hybridization
Nature CommunicationsProcessing spatiotemporal data sources with both high spatial dimension and rich temporal information is a ubiquitous need in machine intelligence. Recurrent neural networks in the machine learning domain and bio-inspired spiking neural networks in the ...
Yujie Wu +7 more
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ELMAN-RECURRENT NEURAL NETWORK FOR LOAD SHEDDING OPTIMIZATION
Jurnal Ilmiah SINERGI, 2020 Load shedding plays a key part in the avoidance of the power system outage. The frequency and voltage fluidity leads to the spread of a power system into sub-systems and leads to the outage as well as the severe breakdown of the system utility.
Widi Aribowo
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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
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Lipschitz Recurrent Neural Networks
, 2020 Published as a conference paper at ICLR ...
N. Benjamin Erichson +4 more
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Recurrent Neural Networks as Electrical Networks, a Formalization
, 2023 Since the 1980s, and particularly with the Hopfield model, recurrent neural networks or RNN became a topic of great interest. The first works of neural networks consisted of simple systems of a few neurons that were commonly simulated through analogue electronic circuits.
Mariano Caruso, Cecilia Jarne
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Learning compact recurrent neural networks [PDF]
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016 Recurrent neural networks (RNNs), including long short-term memory (LSTM) RNNs, have produced state-of-the-art results on a variety of speech recognition tasks. However, these models are often too large in size for deployment on mobile devices with memory and latency constraints. In this work, we study mechanisms for learning compact RNNs and LSTMs via
Zhiyun Lu +2 more
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Recurrent Neural Network Based Narrowband Channel Prediction
, 2006 In this contribution, the application of fully connected recurrent neural networks (FCRNNs) is investigated in the context of narrowband channel prediction.
Liu, W., Yang, L-L., Hanzo, L.
core
Reservoir computing with output feedback
, 2011 Reinhart RF. Reservoir computing with output feedback. Bielefeld: Bielefeld University; 2011.A dynamical system approach to forward and inverse modeling is proposed. Forward and inverse models are trained in associative recurrent neural networks that are
Reinhart, René Felix, Reinhart, Felix
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FEBS Open Bio, EarlyView.In this study, we developed a deep learning method for mitotic figure counting in H&E‐stained whole‐slide images and evaluated its prognostic impact in 13 external validation cohorts from seven different cancer types. Patients with more mitotic figures per mm2 had significantly worse patient outcome in all the studied cancer types except colorectal ...
Joakim Kalsnes +32 more
wiley +1 more source
Two generic mechanisms for emergence of direction selectivity coexist in recurrent neural networks
, 2013 Poster presentation: Twenty Second Annual Computational Neuroscience Meeting: CNS*2013. Paris, France. 13-18 July 2013. In the mammalian visual cortex, the time-averaged response of many neurons is maximal for stimuli moving in a particular direction ...
Matthias Kaschube +3 more
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