25 citations · 61 across the 11 of their papers we have counts for
5 papers · 1 filter
Neuro-GPT: Towards A Foundation Model for EEG
Wenhui Cui, Woojae Jeong, Philipp Thölke +4
To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the power of large publicly available data se…
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs
Wenhui Cui, Haleh Akrami, Anand A. Joshi +1
Despite the remarkable success achieved by graph convolutional networks for functional brain activity analysis, the heterogeneity of functional patterns and the scarcity of imaging…
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…
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…
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…