102 citations · 176 across the 25 of their papers we have counts for
26 papers
AutoHLS: Learning to Accelerate Design Space Exploration for HLS Designs
Md Rubel Ahmed, Toshiaki Koike-Akino, Kieran Parsons +1
High-level synthesis (HLS) is a design flow that leverages modern language features and flexibility, such as complex data structures, inheritance, templates, etc., to prototype har…
Stabilizing Subject Transfer in EEG Classification with Divergence Estimation
Niklas Smedemark-Margulies, Ye Wang, Toshiaki Koike-Akino +4
Classification models for electroencephalogram (EEG) data show a large decrease in performance when evaluated on unseen test sub jects. We reduce this performance decrease using ne…
Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image Synthesis
Nithin Gopalakrishnan Nair, Anoop Cherian, Suhas Lohit +4
Conditional generative models typically demand large annotated training sets to achieve high-quality synthesis. As a result, there has been significant interest in designing models…
Adversarial Bi-Regressor Network for Domain Adaptive Regression
Haifeng Xia, Pu Perry Wang, Toshiaki Koike-Akino +3
Domain adaptation (DA) aims to transfer the knowledge of a well-labeled source domain to facilitate unlabeled target learning. When turning to specific tasks such as indoor (Wi-Fi)…
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…
Learning to Learn Quantum Turbo Detection
Bryan Liu, Toshiaki Koike-Akino, Ye Wang +1
This paper investigates a turbo receiver employing a variational quantum circuit (VQC). The VQC is configured with an ansatz of the quantum approximate optimization algorithm (QAOA…
Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systems
Bryan Liu, Toshiaki Koike-Akino, Ye Wang +1
This paper introduces a new quantum computing framework integrated with a two-step compressed sensing technique, applied to a joint channel estimation and user identification probl…
Multi-Image Visual Question Answering
Harsh Raj, Janhavi Dadhania, Akhilesh Bhardwaj +1
While a lot of work has been done on developing models to tackle the problem of Visual Question Answering, the ability of these models to relate the question to the image features…
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…
MOST-GAN: 3D Morphable StyleGAN for Disentangled Face Image Manipulation
Safa C. Medin, Bernhard Egger, Anoop Cherian +4
Recent advances in generative adversarial networks (GANs) have led to remarkable achievements in face image synthesis. While methods that use style-based GANs can generate striking…
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…
Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders
Mo Han, Ozan Ozdenizci, Toshiaki Koike-Akino +2
Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological st…
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…
Robust Machine Learning via Privacy/Rate-Distortion Theory
Ye Wang, Shuchin Aeron, Adnan Siraj Rakin +2
Robust machine learning formulations have emerged to address the prevalent vulnerability of deep neural networks to adversarial examples. Our work draws the connection between opti…
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…
Disentangled Adversarial Transfer Learning for Physiological Biosignals
Mo Han, Ozan Ozdenizci, Ye Wang +2
Recent developments in wearable sensors demonstrate promising results for monitoring physiological status in effective and comfortable ways. One major challenge of physiological st…
LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood
Abhinav Kumar, Tim K. Marks, Wenxuan Mou +6
Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted loca…
Generative Deep Learning Model for a Multi-level Nano-Optic Broadband Power Splitter
Yingheng Tang, Keisuke Kojima, Toshiaki Koike-Akino +6
We propose a novel Conditional Variational Autoencoder (CVAE) model, enhanced with adversarial censoring and active learning, for the generation of 550 nm broad bandwidth (1250 nm…
Neural Turbo Equalization: Deep Learning for Fiber-Optic Nonlinearity Compensation
Toshiaki Koike-Akino, Ye Wang, David S. Millar +2
Recently, data-driven approaches motivated by modern deep learning have been applied to optical communications in place of traditional model-based counterparts. The application of…
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…
Learning to Modulate for Non-coherent MIMO
Ye Wang, Toshiaki Koike-Akino
The deep learning trend has recently impacted a variety of fields, including communication systems, where various approaches have explored the application of neural networks in pla…
Channel Decoding with Quantum Approximate Optimization Algorithm
Toshiki Matsumine, Toshiaki Koike-Akino, Ye Wang
Motivated by the recent advancement of quantum processors, we investigate quantum approximate optimization algorithm (QAOA) to employ quasi-maximum-likelihood (ML) decoding of clas…
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…