Article-Journal

Improved modeling of human vision by incorporating robustness to blur in convolutional neural networks

Whenever a visual scene is cast onto the retina, much of it will appear degraded due to poor resolution in the periphery; moreover, …

Hojin Jang , Frank Tong

Robustness to Transformations Across Categories: Is Robustness Driven by Invariant Neural Representations?

Deep convolutional neural networks (DCNNs) have demonstrated impressive robustness to recognize objects under transformations (e.g., …

Hojin Jang , Syed Suleman Abbas Zaidi , Xavier Boix , Neeraj Prasad , Sharon Gilad-Gutnick , Shlomit Ben-Ami , Pawan Sinha

Noise-trained deep neural networks effectively predict human vision and its neural responses to challenging images

Deep neural networks (DNNs) for object classification have been argued to provide the most promising model of the visual system, …

Hojin Jang , Devin McCormack , Frank Tong

Convolutional neural networks trained with a developmental sequence of blurry to clear images reveal core differences between face and object processing

Although convolutional neural networks (CNNs) provide a promising model for understanding human vision, most CNNs lack robustness to …

Hojin Jang , Frank Tong

Test–retest reliability of spatial patterns from resting-state functional MRI using the restricted Boltzmann machine and hierarchically organized spatial patterns from the deep belief network

Background Restricted Boltzmann machines (RBMs), including greedy layer-wise trained RBMs as part of a deep belief network (DBN), have …

Kim, Hyun-Chul , Hojin Jang , Jong-Hwan Lee

Developing predictive imaging biomarkers using whole-brain classifiers: Application to the ABIDE I dataset

Within clinical neuroimaging communities there is considerable optimism that functional magnetic resonance imaging (fMRI) will provide …

Rane Swati , Eshin Jolly , Anne Park , Hojin Jang , Cameron Craddock

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks

Feedforward deep neural networks (DNN), artificial neural networks with multiple hidden layers, have recently demonstrated a …

Hojin Jang , Sergey M. Plis , Vince D. Calhoun , Jong-Hwan Lee