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[Submitted on 7 Aug 2024] View a PDF of the paper titled Deep Generative Models for Subgraph Prediction, by Erfaneh Mahmoudzadeh and 3 other authors View PDF HTML (experimental) Abstract:Graph Neural Networks (GNNs) are important across different domains, such as social network analysis and recommendation systems, due to their ability to model complex relational data. This paper introduces subgraph queries as a new task for deep graph learning. Unlike traditional graph prediction tasks that focus on individual components like link prediction or node classification, subgraph queries jointly predict the components of a target subgraph based on evidence that is represented…

[Submitted on 7 Aug 2024] View a PDF of the paper titled AEye: A Visualization Tool for Image Datasets, by Florian Gr"otschla and 3 other authors View PDF HTML (experimental) Abstract:Image datasets serve as the foundation for machine learning models in computer vision, significantly influencing model capabilities, performance, and biases alongside architectural considerations. Therefore, understanding the composition and distribution of these datasets has become increasingly crucial. To address the need for intuitive exploration of these datasets, we propose AEye, an extensible and scalable visualization tool tailored to image datasets. AEye utilizes a contrastively trained model to embed images into semantically…

[Submitted on 7 Aug 2024] View a PDF of the paper titled Human Speech Perception in Noise: Can Large Language Models Paraphrase to Improve It?, by Anupama Chingacham and 3 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) can generate text by transferring style attributes like formality resulting in formal or informal text. However, instructing LLMs to generate text that when spoken, is more intelligible in an acoustically difficult environment, is an under-explored topic. We conduct the first study to evaluate LLMs on a novel task of generating acoustically intelligible paraphrases for better human speech perception in noise.…