3 citations · 6 across the 12 of their papers we have counts for
18 papers
On Stealing Graph Neural Network Models
Marcin Podhajski, Jan Dubiński, Franziska Boenisch +3
Current graph neural network (GNN) model-stealing methods rely heavily on queries to the victim model, assuming no hard query limits. However, in reality, the number of allowed que…
Memorization in Graph Neural Networks
Adarsh Jamadandi, Jing Xu, Adam Dziedzic +1
Deep neural networks (DNNs) have been shown to memorize their training data, yet similar analyses for graph neural networks (GNNs) remain largely under-explored. We introduce NCMem…
Localizing and Mitigating Memorization in Image Autoregressive Models
Aditya Kasliwal, Franziska Boenisch, Adam Dziedzic
Image AutoRegressive (IAR) models have achieved state-of-the-art performance in speed and quality of generated images. However, they also raise concerns about memorization of their…
Demystifying Foreground-Background Memorization in Diffusion Models
Jimmy Z. Di, Yiwei Lu, Yaoliang Yu +3
Diffusion models (DMs) memorize training images and can reproduce near-duplicates during generation. Current detection methods identify verbatim memorization but fail to capture tw…
Adversarial Attacks and Defenses on Graph-aware Large Language Models (LLMs)
Iyiola E. Olatunji, Franziska Boenisch, Jing Xu +1
Large Language Models (LLMs) are increasingly integrated with graph-structured data for tasks like node classification, a domain traditionally dominated by Graph Neural Networks (G…
Implementing Adaptations for Vision AutoRegressive Model
Kaif Shaikh, Franziska Boenisch, Adam Dziedzic
Vision AutoRegressive model (VAR) was recently introduced as an alternative to Diffusion Models (DMs) in image generation domain. In this work we focus on its adaptations, which ai…