CybAI news

[Submitted on 31 Oct 2024 (v1), last revised 22 Nov 2024 (this version, v4)] View a PDF of the paper titled Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure, by Xiang Li and 2 other authors View PDF HTML (experimental) Abstract:In this work, we study the generalizability of diffusion models by looking into the hidden properties of the learned score functions, which are essentially a series of deep denoisers trained on various noise levels. We observe that as diffusion models transition from memorization to generalization, their corresponding nonlinear diffusion denoisers exhibit increasing linearity. This discovery leads us…

arXiv:2411.14479v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated impressive success in a wide range of natural language processing (NLP) tasks due to their extensive general knowledge of the world. Recent works discovered that the performance of LLMs is heavily dependent on the input prompt. However, prompt engineering is usually done manually in a trial-and-error fashion, which can be labor-intensive and challenging in order to find the optimal prompts. To address these problems and unleash the utmost potential of LLMs, we propose a novel LLMs-agnostic framework for prompt optimization, namely GRL-Prompt, which aims to automatically construct optimal prompts…

[Submitted on 12 Nov 2024 (v1), last revised 22 Nov 2024 (this version, v2)] View a PDF of the paper titled Enhancing Link Prediction with Fuzzy Graph Attention Networks and Dynamic Negative Sampling, by Jinming Xing and 1 other authors View PDF HTML (experimental) Abstract:Link prediction is crucial for understanding complex networks but traditional Graph Neural Networks (GNNs) often rely on random negative sampling, leading to suboptimal performance. This paper introduces Fuzzy Graph Attention Networks (FGAT), a novel approach integrating fuzzy rough sets for dynamic negative sampling and enhanced node feature aggregation. Fuzzy Negative Sampling (FNS) systematically selects high-quality negative…

arXiv:2411.14565v1 Announce Type: new Abstract: Video Anomaly Detection (VAD) aims to automatically analyze spatiotemporal patterns in surveillance videos collected from open spaces to detect anomalous events that may cause harm without physical contact. However, vision-based surveillance systems such as closed-circuit television often capture personally identifiable information. The lack of transparency and interpretability in video transmission and usage raises public concerns about privacy and ethics, limiting the real-world application of VAD. Recently, researchers have focused on privacy concerns in VAD by conducting systematic studies from various perspectives including data, features, and systems, making Privacy-Preserving Video Anomaly Detection (P2VAD) a hotspot in…