52 citations · 116 across the 16 of their papers we have counts for
8 papers · 1 filter
AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Data
Niklas Smedemark-Margulies, Ye Wang, Toshiaki Koike-Akino +1
We provide a regularization framework for subject transfer learning in which we seek to train an encoder and classifier to minimize classification loss, subject to a penalty measur…
Equivariant Deep Dynamical Model for Motion Prediction
Bahar Azari, Deniz Erdoğmuş
Learning representations through deep generative modeling is a powerful approach for dynamical modeling to discover the most simplified and compressed underlying description of the…
EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals
Andac Demir, Toshiaki Koike-Akino, Ye Wang +2
Convolutional neural networks (CNN) have been frequently used to extract subject-invariant features from electroencephalogram (EEG) for classification tasks. This approach holds th…
On the Sample Complexity of Rank Regression from Pairwise Comparisons
Berkan Kadioglu, Peng Tian, Jennifer Dy +2
We consider a rank regression setting, in which a dataset of samples with features in is ranked by an oracle via pairwise comparisons. Specifically, there ex…
End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning
Mohammadreza Sharif, Deniz Erdogmus, Christopher Amato +1
State-of-the-art human-in-the-loop robot grasping is hugely suffered by Electromyography (EMG) inference robustness issues. As a workaround, researchers have been looking into inte…
Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing
Haoqing Li, Ricardo Augusto Borsoi, Tales Imbiriba +3
Autoencoder (AEC) networks have recently emerged as a promising approach to perform unsupervised hyperspectral unmixing (HU) by associating the latent representations with the abun…
Inference of Upcoming Human Grasp Using EMG During Reach-to-Grasp Movement
Mo Han, Mehrshad Zandigohar, Sezen Yagmur Gunay +2
Electromyography (EMG) data has been extensively adopted as an intuitive interface for instructing human-robot collaboration. A major challenge of the real-time detection of human…
Multimodal Fusion of EMG and Vision for Human Grasp Intent Inference in Prosthetic Hand Control
Mehrshad Zandigohar, Mo Han, Mohammadreza Sharif +8
Objective: For transradial amputees, robotic prosthetic hands promise to regain the capability to perform daily living activities. Current control methods based on physiological si…