International Conferences

Evaluation of weight sparsity regularization schemes of deep neural networks applied to functional neuroimaging data

Kim HC, Lee JH, 

ICASSP, 2017/Mar, New Orleans, USA
[Oral Presentation]
Mar 6, 2017
Deep learning approaches to functional MRI data analysis

Lee JH

ICMRI, 2017/Mar, Grand Hilton Hotel, Seoul
Mar 6, 2017
Deep learning approach to resting-state networks analysis using fMRI data

Jang H, Lee JH,

SfN 2016, San Diego, CA, USA
Nov 6, 2016
Evaluation of weight sparsity control during autoencoder training of resting-state fMRI using non-zero ratio and Hoyer's sparseness measure

Kim HC, Lee JH, 

PRNI 2016, Trento, Italy
Nov 6, 2016
Reproducibility and hierarchy of weight features from resting fMRI using deep belief network

Jang H, Lee JH,

PRNI 2016, Trento, Italy
Nov 6, 2016
Enhanced pattern distinctness using support vector coefficients than multi-voxel beta values

Kim DY, Lee JH, 

OHBM 2016, Geneva, Switzerland
Nov 6, 2016
Deep learning to predict the emotional response using functional MRI data

Kim HC, Lee JH, 

OHBM 2016, Geneva, Switzerland
Nov 6, 2016
Deep neural network for age prediction using resting-state fMRI data

Jang H, Lee JH, 

OHBM 2016, Geneva, Switzerland
Nov 6, 2016
Neuronal response of electronic cigarette use in comparison to tobacco use: an fMRI study

Heo DW, Jang Y, Kim HC, Lee JH, 

OHBM 2016, Geneva, Switzerland
Nov 6, 2016
Investigation of ERD/ERS of motor imagery and EEG band­specific hemodynamic couplings via EEG­fMRI,

Kim HC, Lee JH, 

OHBM 2015, Honolulu, USA
Nov 6, 2015