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09/14/2023
Beta quantile regression for robust estimation of uncertainty in the presence of outliers
Quantile Regression (QR) can be used to estimate aleatoric uncertainty i...
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12/16/2022
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs
Despite the remarkable success achieved by graph convolutional networks ...
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12/04/2022
Speech MOS multi-task learning and rater bias correction
Perceptual speech quality is an important performance metric for telecon...
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08/26/2022
Learning From Positive and Unlabeled Data Using Observer-GAN
The problem of learning from positive and unlabeled data (A.K.A. PU lear...
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08/08/2022
Learning from imperfect training data using a robust loss function: application to brain image segmentation
Segmentation is one of the most important tasks in MRI medical image ana...
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03/03/2022
Semi-supervised Learning using Robust Loss
The amount of manually labeled data is limited in medical applications, ...
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09/20/2021
Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection
Despite impressive state-of-the-art performance on a wide variety of mac...
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12/13/2020
fMRI-Kernel Regression: A Kernel-based Method for Pointwise Statistical Analysis of rs-fMRI for Population Studies
Due to the spontaneous nature of resting-state fMRI (rs-fMRI) signals, c...
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10/18/2020
Addressing Variance Shrinkage in Variational Autoencoders using Quantile Regression
Estimation of uncertainty in deep learning models is of vital importance...
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06/15/2020
Robust Variational Autoencoder for Tabular Data with Beta Divergence
We propose a robust variational autoencoder with β divergence for tabula...
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10/02/2019
A Pre-defined Sparse Kernel Based Convolution for Deep CNNs
The high demand for computational and storage resources severely impede ...
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10/02/2019
A Pre-defined Sparse Kernel Based Convolutionfor Deep CNNs
The high demand for computational and storage resources severely impede ...
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05/23/2019