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Multicategory Choice Modeling by Recurrent Neural Nets [PDF]

Journal of Retailing and Consumer Services
In multicategory choice, a customer may purchase multiple products or product categories at the same time. Hidden variables of recurrent nets depend on current inputs and hidden variables of the previous period. We investigate the three main variants of recurrent neural nets, which we compare to multilayer perceptrons and multivariate logit models ...
Harald Hruschka
exaly   +5 more sources

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From chaos to clock in recurrent neural net. Case study

Biosystems, 2022
What is the reason for complex dynamical patterns registered from real biological neuronal networks? Noise and dynamical reconfiguring of a network (functional/dynamic connectome) were proposed as possible answers. In this case study, we report a complex dynamical pattern observed in a simple deterministic network of 25 excitatory neurons with fixed ...
Alexander K. Vidybida, Olha Shchur
openaire   +3 more sources

On the identification of recurrent neural nets

Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), 2002
Observational equivalence for so-called Jordan networks, which are a special class of recurrent networks, is analysed. We show this type of neural nets to belong to a wider class of mixed networks and use the description of observational equivalence available for the latter class for obtaining the respective results for the first class.
Dietmar Trummer, Manfred Deistler
openaire   +2 more sources

Asynchronous translations with recurrent neural nets

Proceedings of International Conference on Neural Networks (ICNN'97), 2002
Many researchers have explored the relation between discrete-time recurrent neural networks (DTRNN) and finite-state machines (FSMs) either by showing their computational equivalence or by training them to perform as finite-state recognizers from examples.
Ramón P. Ñeco, Mikel L. Forcada
openaire   +2 more sources

Modelling Landsurface Time-Series With Recurrent Neural Nets

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
Machine learning tools and semi-empirical models have been very successful in describing and predicting instantaneous climatic influences on the spatial and seasonal variability of biosphere state and function. Yet, little work has been carried to explicitly model dynamic features accounting for memory effects, where in some cases hand-designed ...
Markus Reichstein, Simon Besnard, Martin Jung   +2 more
exaly   +4 more sources

The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions

International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, 1998
Recurrent nets are in principle capable to store past inputs to produce the currently desired output. Because of this property recurrent nets are used in time series prediction and process control. Practical applications involve temporal dependencies spanning many time steps, e.g. between relevant inputs and desired outputs.
Sepp Hochreiter
exaly   +2 more sources

A Structure Theory for Identification of Recurrent Neural Nets - Part 1

IFAC Postprint Volumes IPPV / International Federation of Automatic Control, 1998
Abstract This paper extends some results on identifiability of recurrent (dynamic) neural nets obtained by F.Albertini and E.Sontag. A topological and geometrical description of the set of identifiable paranleters is given. In addition it is shown that the classes of observationally equivalent non-nlinimal systems are essentially finite unions of ...
Manfred Deistler
exaly   +2 more sources

Motion analysis with recurrent neural nets

1994
218zVisual tasks such as the interpretation of cell images (Psarrou and Buxton, 1993) and the recognition of moving vehicles require to track objects along their trajectory and to predict their future position in their environment. It was noted that objects move purposely in an environment and effective prediction on their trajectories can be achieved ...
A. Psarrou, H. Buxton
openaire   +1 more source

RACE-Net: A Recurrent Neural Network for Biomedical Image Segmentation

IEEE Journal of Biomedical and Health Informatics, 2019
The level set based deformable models (LDM) are commonly used for medical image segmentation. However, they rely on a handcrafted curve evolution velocity that needs to be adapted for each segmentation task. The Convolutional Neural Networks (CNN) address this issue by learning robust features in a supervised end-to-end manner.
Arunava Chakravarty, Jayanthi Sivaswamy
openaire   +3 more sources