Results 111 to 120 of about 3,974,691 (210)
Reversible Recurrent Neural Networks
CoRR, 2018 Recurrent neural networks (RNNs) provide state-of-the-art performance in processing sequential data but are memory intensive to train, limiting the flexibility of RNN models which can be trained. Reversible RNNs---RNNs for which the hidden-to-hidden transition can be reversed---offer a path to reduce the memory requirements of training, as hidden ...
Matthew MacKay +3 more
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Monostable controllers for adaptive behavior
, 2008 Recent artificial neural networks for machine learning have exploited transient dynamics around globally stable attractors, inspired by the properties of cortical microcolumns.
Fine, Peter +3 more
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Recurrent Neural Networks and Soft Computing [PDF]
, 2012 New applications in recurrent neural networks are covered by this book, which will be required reading in the field. Methodological tools covered include ranking indices for fuzzy numbers, a neuro-fuzzy digital filter and mapping graphs of parallel ...
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Contextual Recurrent Neural Networks
CoRR, 2019 There is an implicit assumption that by unfolding recurrent neural networks (RNN) in finite time, the misspecification of choosing a zero value for the initial hidden state is mitigated by later time steps. This assumption has been shown to work in practice and alternative initialization may be suggested but often overlooked.
Sam Wenke, Jim Fleming
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Brain-inspired, interpretable, resonant recurrent neural networks
Physical Review ResearchTraditional artificial neural networks consist of nodes with nonoscillatory dynamics. Biological neural networks, on the other hand, consist of oscillatory components embedded in an oscillatory environment. Motivated by this feature of biological neurons,
Mark A. Kramer
doaj +1 more source
Lazy training of radial basis neural networks [PDF]
, 2006 Proceeding of: 16th International Conference on Artificial Neural Networks, ICANN 2006. Athens, Greece, September 10-14, 2006Usually, training data are not evenly distributed in the input space.
Galván, Inés M. +5 more
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Pixel Recurrent Neural Networks
CoRR, 2016 Modeling the distribution of natural images is a landmark problem in unsupervised learning. This task requires an image model that is at once expressive, tractable and scalable. We present a deep neural network that sequentially predicts the pixels in an image along the two spatial dimensions. Our method models the discrete probability of the raw pixel
Aäron van den Oord +2 more
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08041 Summary – Recurrent Neural Networks - Models, Capacities, and Applications [PDF]
, 2008 The seminar centered around recurrent information processing in neural systems and its connections to brain sciences, on the one hand, and higher symbolic reasoning, on the other side. The goal was to explore connections across the disciplines and to
Maass, Wolfgang +3 more
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Sliced Recurrent Neural Networks
CoRR, 2018 12 pages (including references), 2 figures, 3 tables, conference: The 27th International Conference on Computational Linguistics (COLING 2018)
Zeping Yu, Gongshen Liu
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A lecture transcription system combining neural network acoustic and language models [PDF]
, 2013 This paper presents a new system for automatic transcription of lectures. The system combines a number of novel features, including deep neural network acoustic models using multi-level adaptive networks to incorporate out-of-domain information, and ...
Hori, C +6 more
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