Search Results for author:
Found 8 papers, 3 papers with code
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex
In this work, assuming that the exact posterior or a decent approximation is obtained, we propose a generic framework to approximate the output probability distribution induced by model posterior with a parameterized model and in an amortized fashion.
Fast Scenario Reduction for Power Systems by Deep Learning
The output of the DCNN will be an "image" of the reduced scenario set.
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over the Simplex
In this work, assuming that the exact posterior or a decent approximation is obtained, we propose a generic framework to approximate the output probability distribution induced by model posterior with a parameterized model and in an amortized fashion.
Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data
The results indicate that the VAE model is a promising approach for actigraphy data analysis for mental health outcomes in long-term studies.
Classification of 12-lead ECGs: the PhysioNet/ Computing in Cardiology Challenge 2020
Main results: A total of 217 teams submitted 1395 algorithms during the Challenge, representing a diversity of approaches for identifying cardiac abnormalities from both academia and industry.
Variational Nested Dropout
Nested dropout is a variant of dropout operation that is able to order network parameters or features based on the pre-defined importance during training.
A Data-Driven Gaussian Process Filter for Electrocardiogram Denoising
Objective: Gaussian Processes (GP)-based filters, which have been effectively used for various applications including electrocardiogram (ECG) filtering can be computationally demanding and the choice of their hyperparameters is typically ad hoc.
Model Will Tell: Training Membership Inference for Diffusion Models
In this paper, we explore a novel perspective for the TMI task by leveraging the intrinsic generative priors within the diffusion model.
