102 citations · 331 across the 58 of their papers we have counts for
8 papers · 1 filter
Exploring User-level Gradient Inversion with a Diffusion Prior
Zhuohang Li, Andrew Lowy, Jing Liu +4
We explore user-level gradient inversion as a new attack surface in distributed learning. We first investigate existing attacks on their ability to make inferences about private in…
Hybrid Quantum-Classical Neural Networks for Downlink Beamforming Optimization
Juping Zhang, Gan Zheng, Toshiaki Koike-Akino +2
This paper investigates quantum machine learning to optimize the beamforming in a multiuser multiple-input single-output downlink system. We aim to combine the power of quantum neu…
Variational Randomized Smoothing for Sample-Wise Adversarial Robustness
Ryo Hase, Ye Wang, Toshiaki Koike-Akino +2
Randomized smoothing is a defensive technique to achieve enhanced robustness against adversarial examples which are small input perturbations that degrade the performance of neural…
Efficient Differentially Private Fine-Tuning of Diffusion Models
Jing Liu, Andrew Lowy, Toshiaki Koike-Akino +2
The recent developments of Diffusion Models (DMs) enable generation of astonishingly high-quality synthetic samples. Recent work showed that the synthetic samples generated by the…
TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models
Haomiao Ni, Bernhard Egger, Suhas Lohit +5
Text-conditioned image-to-video generation (TI2V) aims to synthesize a realistic video starting from a given image (e.g., a woman's photo) and a text description (e.g., "a woman is…
SuperLoRA: Parameter-Efficient Unified Adaptation of Multi-Layer Attention Modules
Xiangyu Chen, Jing Liu, Ye Wang +4
Low-rank adaptation (LoRA) and its variants are widely employed in fine-tuning large models, including large language models for natural language processing and diffusion models fo…
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…
Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?
Andrew Lowy, Zhuohang Li, Jing Liu +3
For small privacy parameter , -differential privacy (DP) provides a strong worst-case guarantee that no membership inference attack (MIA) can succeed at determining whether a…