In the chapter, a four-level hierarchical structure with letters, words, sentences, and strophes of the popular song "This land is your land" are simulated to illustrate the proposed hierarchical sequence learning model. learning systems; neural nets...
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, ...
Visualizing children’s stories is an important task to support distance learning and education during the current COVID-19 pandemic outbreak. This task can be regarded as a rapid transition to overcome any significant disruption to the provision of visu
The Journal of Machine Learning Research 21(1):5485–5551 MathSciNet Google Scholar Narayan S, Cohen SB, Lapata M (2018) Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization, in Proceedings of the 2018 Conference on Empirical ...
[15] S. Hochreiter, Y. Bengio, P. Frasconi, and J. Schmidhuber. Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-term Dependencies. In S. C. Kremer and J. F. Kolen, editors, A Field Guide to Dynamical Recurrent Neural Networks. 2001. ...
Recurrent Nets: the Difficulty of Learning Long-term Dependencies. In S. C.Kremer and J. F. Kolen, editors, A Field Guide to Dynamical RecurrentNeural Networks. IEEE Press, 2001b. Hochreiter and J. Schmidhuber. Long Short-Term Memory. Neural Computation, 9(8):1735{1780, 1997. F. Gers...
learning systemsneural nets/ temporal sequence storageGardner's analysisneural netpattern storageleaky integrator neuronscapacity/ C1230 Artificial intelligence C1240 Adaptive system theoryGardner's analysis of the capacity of a neural net for pattern storage is extended to the case of the storage of ...
Decoding the protein–ligand interactions using parallel graph neural networks Article Open access 10 May 2022 Learning characteristics of graph neural networks predicting protein–ligand affinities Article 13 November 2023 Generic protein–ligand interaction scoring by integrating physical prior knowledge...
Today, most commonly used deep learning algorithms for synthetic biology are broadly derived from either computer vision or natural language processing (NLP) based approaches22. Convolutional neural nets (CNNs)23 comprise the backbone of most computer vision-based algorithms and excel at elucidating ...
[15] S. Hochreiter, Y. Bengio, P. Frasconi, and J. Schmidhuber. Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-term Dependencies. In S. C. Kremer and J. F. Kolen, editors, A Field Guide to Dynamical Recurrent Neural Networks. 2001. ...