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…