Results 41 to 50 of about 30,189 (295)
What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator? [PDF]
Proceedings of the 10th International Conference on Natural Language Generation, 2017 Appears in: Proceedings of the 10th International Conference on Natural Language Generation (INLG'17)
Tanti, Marc +3 more
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Molecular Generation with Recurrent Neural Networks (RNNs)
CoRR, 2017 The potential number of drug like small molecules is estimated to be between 10^23 and 10^60 while current databases of known compounds are orders of magnitude smaller with approximately 10^8 compounds. This discrepancy has led to an interest in generating virtual libraries using hand crafted chemical rules and fragment based methods to cover a larger ...
Esben Jannik Bjerrum, Richard Threlfall
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Recurrent Neural Network-Based Nonlinear Optimization for Braking Control of Electric Vehicles
Energies, 2022 In this paper, electro-hydraulic braking (EHB) force allocation for electric vehicles (EVs) is modeled as a constrained nonlinear optimization problem (NOP).
Jiapeng Yan, Huifang Kong, Zhihong Man
doaj +1 more source
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN) [PDF]
, 2015 In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. It directly models the probability distribution of generating a word given previous words and an image. Image captions are generated according to this distribution.
Mao, Junhua +5 more
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Memory augmented recurrent neural networks for de-novo drug design.
PLoS ONE, 2022 A recurrent neural network (RNN) is a machine learning model that learns the relationship between elements of an input series, in addition to inferring a relationship between the data input to the model and target output.
Naveen Suresh +3 more
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Sparse RNNs can support high-capacity classification.
PLoS Computational Biology, 2022 Feedforward network models performing classification tasks rely on highly convergent output units that collect the information passed on by preceding layers.
Denis Turcu, L F Abbott
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Human EEG and Recurrent Neural Networks Exhibit Common Temporal Dynamics During Speech Recognition
Frontiers in Systems Neuroscience, 2021 Recent deep-learning artificial neural networks have shown remarkable success in recognizing natural human speech, however the reasons for their success are not entirely understood.
Saeedeh Hashemnia +3 more
doaj +1 more source
, 2020 Detail of the hyperparameters retained for Multi-layer Perceptron (MLP), convolutional neural network (ConvNet), recurrent neural network (RNN) and DeepConvLSTM.
Tomoya Suzuki (562515) +2 more
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Comparing forecasting performances between multilayer feedforward neural network and recurrent neural network in Malaysia's load [PDF]
, 2010 This paper presents the use of two artificial neural networks models, namely the multilayer feedforward neural network (MLFF) and the recurrent neural network (RNN) are applied for Malaysia’s load forecasting. For this purpose, a half hourly load data is
Ismail, Zuhaimy +7 more
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Predictable non-linearities in U.S. inflation [PDF]
, 2006 We expand Nakamura’s (2005) neural network based inflation forecasting experiment to an alternative non-linear model; a Markov switching autoregressive (MS-AR) model.
Binner, J +10 more
core +1 more source