Results 11 to 20 of about 99,773 (316)
Shuffling Recurrent Neural Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 We propose a novel recurrent neural network model, where the hidden state hₜ is obtained by permuting the vector elements of the previous hidden state hₜ₋₁ and adding the output of a learned function β(xₜ) of the input xₜ at time t. In our model, the prediction is given by a second learned function, which is applied to the hidden state s(hₜ).
Michael Rotman, Lior Wolf
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Discontinuities in Recurrent Neural Networks [PDF]
Neural Computation, 1999 This article studies the computational power of various discontinuous real computational models that are based on the classical analog recurrent neural network (ARNN). This ARNN consists of finite number of neurons; each neuron computes a polynomial net function and a sigmoid-like continuous activation function.
Ricard Gavaldà, Hava T. Siegelmann
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Recurrent Neural Network Grammars [PDF]
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016 We introduce recurrent neural network grammars, probabilistic models of sentences with explicit phrase structure. We explain efficient inference procedures that allow application to both parsing and language modeling. Experiments show that they provide better parsing in English than any single previously published supervised generative model and better
Dyer, Chris +3 more
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Restricted Recurrent Neural Networks [PDF]
2019 IEEE International Conference on Big Data (Big Data), 2019 Recurrent Neural Network (RNN) and its variations such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), have become standard building blocks for learning online data of sequential nature in many research areas, including natural language processing and speech data analysis.
Enmao Diao, Jie Ding 0002, Vahid Tarokh
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Deductron—A Recurrent Neural Network [PDF]
Frontiers in Applied Mathematics and Statistics, 2020 34 pages, contains Python code, Python code requires data file data.py (should be included in the archive)
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A Deep Multi-Task Learning Approach for Bioelectrical Signal Analysis
Mathematics, 2023 Deep learning is a promising technique for bioelectrical signal analysis, as it can automatically discover hidden features from raw data without substantial domain knowledge.
Jishu K. Medhi +3 more
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Prediction of Convergence Dynamics of Design Performance using Differential Recurrent Neural Networks [PDF]
, 2008 Computational Fluid Dynamics (CFD) simulations have been extensively used in many aerodynamic design optimization problems, such as wing and turbine blade shape design optimization.
Sendhoff, Bernhard +7 more
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Journal of Advances in Modeling Earth Systems, 2023 Systematic biases in numerical weather prediction models cause forecast deviation from reality. While model biases also affect data assimilation and degrade the analysis accuracy, observation information incorporated through data assimilation can provide
A. Amemiya, M. Shlok, T. Miyoshi
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Medical Image Interpolation Using Recurrent Type-2 Fuzzy Neural Network
Frontiers in Neuroinformatics, 2021 Image interpolation is an essential process for image processing and computer graphics in wide applications to medical imaging. For image interpolation used in medical diagnosis, the two-dimensional (2D) to three-dimensional (3D) transformation can ...
Jafar Tavoosi +5 more
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