216 citations · 816 across the 46 of their papers we have counts for
8 papers · 1 filter
Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders
Ozan Ozdenizci, Ye Wang, Toshiaki Koike-Akino +1
We introduce adversarial neural networks for representation learning as a novel approach to transfer learning in brain-computer interfaces (BCIs). The proposed approach aims to lea…
Deep feature transfer between localization and segmentation tasks
Szu-Yeu Hu, Andrew Beers, Ken Chang +8
In this paper, we propose a new pre-training scheme for U-net based image segmentation. We first train the encoding arm as a localization network to predict the center of the targe…
Time-Series Prediction of Proximal Aggression Onset in Minimally-Verbal Youth with Autism Spectrum Disorder Using Physiological Biosignals
Ozan Ozdenizci, Catalina Cumpanasoiu, Carla Mazefsky +4
It has been suggested that changes in physiological arousal precede potentially dangerous aggressive behavior in youth with autism spectrum disorder (ASD) who are minimally verbal…
Hierarchical Graphical Models for Context-Aware Hybrid Brain-Machine Interfaces
Ozan Ozdenizci, Sezen Yagmur Gunay, Fernando Quivira +1
We present a novel hierarchical graphical model based context-aware hybrid brain-machine interface (hBMI) using probabilistic fusion of electroencephalographic (EEG) and electromyo…
Structured Adversarial Attack: Towards General Implementation and Better Interpretability
Kaidi Xu, Sijia Liu, Pu Zhao +6
When generating adversarial examples to attack deep neural networks (DNNs), Lp norm of the added perturbation is usually used to measure the similarity between original image and a…
Invariant Representations from Adversarially Censored Autoencoders
Ye Wang, Toshiaki Koike-Akino, Deniz Erdogmus
We combine conditional variational autoencoders (VAE) with adversarial censoring in order to learn invariant representations that are disentangled from nuisance/sensitive variation…
Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection
Seyed Raein Hashemi, Seyed Sadegh Mohseni Salehi, Deniz Erdogmus +3
Fully convolutional deep neural networks have been asserted to be fast and precise frameworks with great potential in image segmentation. One of the major challenges in training su…
Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration
Seyed Sadegh Mohseni Salehi, Shadab Khan, Deniz Erdogmus +1
With an aim to increase the capture range and accelerate the performance of state-of-the-art inter-subject and subject-to-template 3D registration, we propose deep learning-based m…