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, ...
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...
[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. [16] S. Hochreiter and J. Sch...
This article introduces a neural network capable of learning a temporal sequence. Directly inspired from a hippocampus model [2], this architecture allows an autonomous robot to learn how to imitate a sequence of movements with the correct timing. The results show that the network model is fast,...
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...
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
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)23comprise the backbone of most computer vision-based algorithms and excel at elucidating important se...
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...
This paper proposes a new route for applying the generative adversarial nets (GANs) to NLP tasks (taking the neural machine translation as an instance) and the widespread perspective that GANs can't work well in the NLP area turns out to be unreasonable. In this work, we build a conditional...
Designing promoters with desirable properties is essential in synthetic biology. Human experts are skilled at identifying strong explicit patterns in small samples, while deep learning models excel at detecting implicit weak patterns in large datasets. Biologists have described the sequence patterns of prom...