Results 101 to 110 of about 7,083,147 (335)
Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting
Water, 2020 Due to the stochastic nature and complexity of flow, as well as the existence of hydrological uncertainties, predicting streamflow in dam reservoirs, especially in semi-arid and arid areas, is essential for the optimal and timely use of surface water ...
Halit Apaydin +5 more
semanticscholar +1 more source
Grounded Recurrent Neural Networks
CoRR, 2017 In this work, we present the Grounded Recurrent Neural Network (GRNN), a recurrent neural network architecture for multi-label prediction which explicitly ties labels to specific dimensions of the recurrent hidden state (we call this process "grounding"). The approach is particularly well-suited for extracting large numbers of concepts from text.
Ankit Vani +2 more
openaire +2 more sources
Molecular Oncology, EarlyView.Arginine methylation can be viewed as a persistence‐prone post‐translational modification regulated by a network of PRMTs. Competitive and compensatory interactions among PRMTs can redistribute methylation across substrate pools shaped by sequence, structural, spatial, and environmental layers, reinforcing RNA‐processing, chromatin, and signaling ...
So Hyun Kwon, Ji Min Lee
wiley +1 more source
Universal approximation property of stochastic configuration networks for time series
Industrial Artificial IntelligenceFor the purpose of processing sequential data, such as time series, and addressing the challenge of manually tuning the architecture of traditional recurrent neural networks (RNNs), this paper introduces a novel approach-the Recurrent Stochastic ...
Jin-Xi Zhang, Hangyi Zhao, Xuefeng Zhang
doaj +1 more source
Segmental Recurrent Neural Networks
, 2015 We introduce segmental recurrent neural networks (SRNNs) which define, given an input sequence, a joint probability distribution over segmentations of the input and labelings of the segments. Representations of the input segments (i.e., contiguous subsequences of the input) are computed by encoding their constituent tokens using bidirectional recurrent
Lingpeng Kong, Chris Dyer, Noah A. Smith
openaire +2 more sources
Directed evolution of enzymes at the crossroads of tradition and innovation
FEBS Open Bio, EarlyView.An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova +2 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 +3 more
openaire +5 more sources
Loss of AMBRA1 activates MAPK and angiogenesis signaling pathways in melanoma cells
FEBS Open Bio, EarlyView.Loss of AMBRA1 in melanoma cells activates multiple oncogenic pathways associated with tumor progression. Transcriptomic and protein network analyses revealed that AMBRA1 depletion enhances MAPK/ERK signaling, angiogenesis, TGF‐β/EMT signaling, and Wnt/axon guidance pathways.
Milad Ibrahim +4 more
wiley +1 more source
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 +3 more
doaj +1 more source