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Found 5 papers, 2 papers with code
A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional Data
This research proposes a novel non-parametric modeling approach, leveraging the Gaussian process (GP), to characterize high-dimensional data by mapping it to a latent low-dimensional manifold.
Bayesian Time-Series Classifier for Decoding Simple Visual Stimuli from Intracranial Neural Activity
In addition to its high classification accuracy, the proposed BTsC model provides interpretable results, making the technique a valuable tool to study neural activity in various tasks and categories.
Deep Direct Discriminative Decoders for High-dimensional Time-series Data Analysis
The D4 brings deep neural networks' expressiveness and scalability to the SSM formulation letting us build a novel solution that efficiently estimates the underlying state processes through high-dimensional observation signal.
Latent Factor Decomposition Model: Applications for Questionnaire Data
The analysis of clinical questionnaire data comes with many inherent challenges.
Foot anthropometry device and single object image thresholding
This paper introduces a device, algorithm and graphical user interface to obtain anthropometric measurements of foot.
