102 citations · 134 across the 14 of their papers we have counts for
7 papers · 1 filter
AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications
Toshiaki Koike-Akino, Pu Wang, Ye Wang
Commercial Wi-Fi devices can be used for integrated sensing and communications (ISAC) to jointly exchange data and monitor indoor environment. In this paper, we investigate a proof…
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…
Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction
Mo Han, Ozan Ozdenizci, Ye Wang +2
Recent developments in biosignal processing have enabled users to exploit their physiological status for manipulating devices in a reliable and safe manner. One major challenge of…
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference
Andac Demir, Toshiaki Koike-Akino, Ye Wang +1
Learning data representations that capture task-related features, but are invariant to nuisance variations remains a key challenge in machine learning. We introduce an automated Ba…
Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction
Toshiaki Koike-Akino, Ye Wang
We propose a new concept of rateless auto-encoders (RL-AEs) that enable a flexible latent dimensionality, which can be seamlessly adjusted for varying distortion and dimensionality…
Adversarial Deep Learning in EEG Biometrics
Ozan Ozdenizci, Ye Wang, Toshiaki Koike-Akino +1
Deep learning methods for person identification based on electroencephalographic (EEG) brain activity encounters the problem of exploiting the temporally correlated structures or r…