Search Results for author: Bo Hui

Found 9 papers, 6 papers with code

A Survey of Lottery Ticket Hypothesis

no code implementations7 Mar 2024 Bohan Liu, Zijie Zhang, Peixiong He, Zhensen Wang, Yang Xiao, Ruimeng Ye, Yang Zhou, Wei-Shinn Ku, Bo Hui

The Lottery Ticket Hypothesis (LTH) states that a dense neural network model contains a highly sparse subnetwork (i. e., winning tickets) that can achieve even better performance than the original model when trained in isolation.

Successfully Applying Lottery Ticket Hypothesis to Diffusion Model

1 code implementation28 Oct 2023 Chao Jiang, Bo Hui, Bohan Liu, Da Yan

Therefore, we propose to find the winning ticket with varying sparsity along different layers in the model.


SneakyPrompt: Jailbreaking Text-to-image Generative Models

1 code implementation20 May 2023 Yuchen Yang, Bo Hui, Haolin Yuan, Neil Gong, Yinzhi Cao

Text-to-image generative models such as Stable Diffusion and DALL$\cdot$E raise many ethical concerns due to the generation of harmful images such as Not-Safe-for-Work (NSFW) ones.

Reinforcement Learning (RL) Semantic Similarity +1

Rethinking Graph Lottery Tickets: Graph Sparsity Matters

no code implementations3 May 2023 Bo Hui, Da Yan, Xiaolong Ma, Wei-Shinn Ku

Therefore, we propose two techniques to improve GNN performance when the graph sparsity is high.

RFID-Based Indoor Spatial Query Evaluation with Bayesian Filtering Techniques

1 code implementation2 Apr 2022 Bo Hui, Wenlu Wang, Jiao Yu, Zhitao Gong, Wei-Shinn Ku, Min-Te Sun, Hua Lu

Based on the inference method and tracking models, we develop innovative indoor range and k nearest neighbor (kNN) query algorithms.

CDGNet: A Cross-Time Dynamic Graph-based Deep Learning Model for Traffic Forecasting

no code implementations6 Dec 2021 Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Liang Zeng, Bo Hui, Chenxing Wang

Besides, we propose a novel encoder-decoder architecture to incorporate the cross-time dynamic graph-based GCN for multi-step traffic forecasting.

Practical Blind Membership Inference Attack via Differential Comparisons

1 code implementation5 Jan 2021 Bo Hui, Yuchen Yang, Haolin Yuan, Philippe Burlina, Neil Zhenqiang Gong, Yinzhi Cao

The success of the former heavily depends on the quality of the shadow model, i. e., the transferability between the shadow and the target; the latter, given only blackbox probing access to the target model, cannot make an effective inference of unknowns, compared with MI attacks using shadow models, due to the insufficient number of qualified samples labeled with ground truth membership information.

Inference Attack Membership Inference Attack

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