2 citations · 7 across the 10 of their papers we have counts for
10 papers
Structured Unitary Tensor Network Representations for Circuit-Efficient Quantum Data Encoding
Guang Lin, Toshihisa Tanaka, Qibin Zhao
Encoding classical data into quantum states is a central bottleneck in quantum machine learning: many widely used encodings are circuit-inefficient, requiring deep circuits and sub…
Parameter-Efficient Fine-Tuning of 3D DDPM for MRI Image Generation Using Tensor Networks
Binghua Li, Ziqing Chang, Tong Liang +5
We address the challenge of parameter-efficient fine-tuning (PEFT) for three-dimensional (3D) U-Net-based denoising diffusion probabilistic models (DDPMs) in magnetic resonance ima…
Model-Free Adversarial Purification via Coarse-To-Fine Tensor Network Representation
Guang Lin, Duc Thien Nguyen, Zerui Tao +3
Deep neural networks are known to be vulnerable to well-designed adversarial attacks. Although numerous defense strategies have been proposed, many are tailored to the specific att…
Scalable Bayesian Tensor Ring Factorization for Multiway Data Analysis
Zerui Tao, Toshihisa Tanaka, Qibin Zhao
Tensor decompositions play a crucial role in numerous applications related to multi-way data analysis. By employing a Bayesian framework with sparsity-inducing priors, Bayesian Ten…
Large Language Model Sentinel: LLM Agent for Adversarial Purification
Guang Lin, Toshihisa Tanaka, Qibin Zhao
Over the past two years, the use of large language models (LLMs) has advanced rapidly. While these LLMs offer considerable convenience, they also raise security concerns, as LLMs a…
MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor Classification
Binghua Li, Jie Mao, Zhe Sun +3
Automated diagnosis with artificial intelligence has emerged as a promising area in the realm of medical imaging, while the interpretability of the introduced deep neural networks…
Adversarial Guided Diffusion Models for Adversarial Purification
Guang Lin, Zerui Tao, Jianhai Zhang +2
Diffusion model (DM) based adversarial purification (AP) has proven to be a powerful defense method that can remove adversarial perturbations and generate a purified example withou…
Efficient Nonparametric Tensor Decomposition for Binary and Count Data
Zerui Tao, Toshihisa Tanaka, Qibin Zhao
In numerous applications, binary reactions or event counts are observed and stored within high-order tensors. Tensor decompositions (TDs) serve as a powerful tool to handle such hi…
EpilepsyLLM: Domain-Specific Large Language Model Fine-tuned with Epilepsy Medical Knowledge
Xuyang Zhao, Qibin Zhao, Toshihisa Tanaka
With large training datasets and massive amounts of computing sources, large language models (LLMs) achieve remarkable performance in comprehensive and generative ability. Based on…
Adversarial Training on Purification (AToP): Advancing Both Robustness and Generalization
Guang Lin, Chao Li, Jianhai Zhang +2
The deep neural networks are known to be vulnerable to well-designed adversarial attacks. The most successful defense technique based on adversarial training (AT) can achieve optim…