activity
20192022
most citedA Pre-defined Sparse Kernel Based Convolution for Deep CNNs

5 citations · 12 across the 5 of their papers we have counts for

collaborators

7 papers

cs.SD2022

Speech MOS multi-task learning and rater bias correction

Haleh Akrami, Hannes Gamper

Perceptual speech quality is an important performance metric for teleconferencing applications. The mean opinion score (MOS) is standardized for the perceptual evaluation of speech…

cs.LG20222 cited

Semi-supervised Learning using Robust Loss

Wenhui Cui, Haleh Akrami, Anand A. Joshi +1

The amount of manually labeled data is limited in medical applications, so semi-supervised learning and automatic labeling strategies can be an asset for training deep neural netwo…

eess.SP2020

fMRI-Kernel Regression: A Kernel-based Method for Pointwise Statistical Analysis of rs-fMRI for Population Studies

Anand A. Joshi, Soyoung Choi, Haleh Akrami +1

Due to the spontaneous nature of resting-state fMRI (rs-fMRI) signals, cross-subject comparison and therefore, group studies of rs-fMRI are challenging. Most existing group compari…

cs.LG2020

Addressing Variance Shrinkage in Variational Autoencoders using Quantile Regression

Haleh Akrami, Anand A. Joshi, Sergul Aydore +1

Estimation of uncertainty in deep learning models is of vital importance, especially in medical imaging, where reliance on inference without taking into account uncertainty could l…

cs.LG20205 cited

Robust Variational Autoencoder for Tabular Data with Beta Divergence

Haleh Akrami, Sergul Aydore, Richard M. Leahy +1

We propose a robust variational autoencoder with divergence for tabular data (RTVAE) with mixed categorical and continuous features. Variational autoencoders (VAE) and their va…

cs.CV20195 cited

A Pre-defined Sparse Kernel Based Convolution for Deep CNNs

Souvik Kundu, Saurav Prakash, Haleh Akrami +2

The high demand for computational and storage resources severely impede the deployment of deep convolutional neural networks (CNNs) in limited-resource devices. Recent CNN architec…