Search Results for author:
Found 5 papers, 1 papers with code
Latent Variable Double Gaussian Process Model for Decoding Complex Neural Data
Non-parametric models, such as Gaussian Processes (GP), show promising results in the analysis of complex data.
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.
Reverse Survival Model (RSM): A Pipeline for Explaining Predictions of Deep Survival Models
The aim of survival analysis in healthcare is to estimate the probability of occurrence of an event, such as a patient's death in an intensive care unit (ICU).
Survival Seq2Seq: A Survival Model based on Sequence to Sequence Architecture
This paper introduces a novel non-parametric deep model for estimating time-to-event (survival analysis) in presence of censored data and competing risks.
