Search Results for author:
Found 10 papers, 4 papers with code
Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing
The model is learned end-to-end using stochastic backpropagation, and trained using a self-supervised strategy which leverages benefits from semi-supervised learning techniques.
Fetal-BET: Brain Extraction Tool for Fetal MRI
Evaluations on independent test data show that our method achieves accurate brain extraction on heterogeneous test data acquired with different scanners, on pathological brains, and at various gestational stages.
User Training with Error Augmentation for Electromyogram-based Gesture Classification
We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wrist-band configuration.
A Multi-label Classification Approach to Increase Expressivity of EMG-based Gesture Recognition
Main Results: We found that a problem transformation approach using a parallel model architecture in combination with a non-linear classifier, along with restricted synthetic data generation, shows promise in increasing the expressivity of sEMG-based gestures with a short calibration time.
A Vision for Cleaner Rivers: Harnessing Snapshot Hyperspectral Imaging to Detect Macro-Plastic Litter
To address this problem, we analyze the feasibility of macro-plastic litter detection using computational imaging approaches in river-like scenarios.
Hybrid Neural Network Augmented Physics-based Models for Nonlinear Filtering
In this paper we present a hybrid neural network augmented physics-based modeling (APBM) framework for Bayesian nonlinear latent space estimation.
Equivariant Deep Dynamical Model for Motion Prediction
Learning representations through deep generative modeling is a powerful approach for dynamical modeling to discover the most simplified and compressed underlying description of the data, to then use it for other tasks such as prediction.
Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing
Autoencoder (AEC) networks have recently emerged as a promising approach to perform unsupervised hyperspectral unmixing (HU) by associating the latent representations with the abundances, the decoder with the mixing model and the encoder with its inverse.
Prediction of Epilepsy Development in Traumatic Brain Injury Patients from Diffusion Weighted MRI
Post-traumatic epilepsy (PTE) is a life-long complication of traumatic brain injury (TBI) and is a major public health problem that has an estimated incidence that ranges from 2%-50%, depending on the severity of the TBI.
Mapping Motor Cortex Stimulation to Muscle Responses: A Deep Neural Network Modeling Approach
In this work, potential DNN models are explored and the one with the minimum squared errors is recommended for the optimal performance of the M2M-Net, a network that maps transcranial magnetic stimulation of the motor cortex to corresponding muscle responses, using: a finite element simulation, an empirical neural response profile, a convolutional autoencoder, a separate deep network mapper, and recordings of multi-muscle activation.
