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Hybrid Backpropagation Parallel Reservoir Networks
In many real-world applications, fully-differentiable RNNs such as LSTMs...
10/27/2020 ∙ by Matthew Evanusa, et al. ∙
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Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems
We consider the commonly encountered situation (e.g., in weather forecas...
02/10/2020 ∙ by Alexander Wikner, et al. ∙
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Forecasting of Spatio-temporal Chaotic Dynamics with Recurrent Neural Networks: a comparative study of Reservoir Computing and Backpropagation Algorithms
How effective are Recurrent Neural Networks (RNNs) in forecasting the sp...
10/09/2019 ∙ by Pantelis R. Vlachas, et al. ∙
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Identifying and Predicting Parkinson's Disease Subtypes through Trajectory Clustering via Bipartite Networks
Parkinson's disease (PD) is a common neurodegenerative disease with a hi...
06/12/2019 ∙ by Sanjukta Krishnagopal, et al. ∙
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Computational landscape of user behavior on social media
With the increasing abundance of 'digital footprints' left by human inte...
01/25/2019 ∙ by David Darmon, et al. ∙
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Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model
A model-based approach to forecasting chaotic dynamical systems utilizes...
03/09/2018 ∙ by Jaideep Pathak, et al. ∙
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Understanding the Predictive Power of Computational Mechanics and Echo State Networks in Social Media
There is a large amount of interest in understanding users of social med...
06/26/2013 ∙ by David Darmon, et al. ∙
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