Academic
arXiv:2602.17071v1 Announce Type: new Abstract: Graph neural networks frequently encounter significant performance degradation when confronted with structural noise or non-homophilous topologies. To address these systemic …
arXiv:2602.17080v1 Announce Type: new Abstract: Efficient stochastic optimization typically integrates an update direction that performs well in the deterministic regime with a mechanism adapting to …
arXiv:2602.17088v1 Announce Type: new Abstract: The growing concern over training data privacy has elevated the "Right to be Forgotten" into a critical requirement, thereby raising …
arXiv:2602.17089v1 Announce Type: new Abstract: Diffusion models recently developed for generative AI tasks can produce high-quality samples while still maintaining diversity among samples to promote …
arXiv:2602.17092v1 Announce Type: new Abstract: Foreign key discovery and related schema-level prediction tasks are often modeled using graph neural networks (GNNs), implicitly assuming that relational …
arXiv:2602.17095v1 Announce Type: new Abstract: Parameter-efficient fine-tuning techniques such as low-rank adaptation (LoRA) enable large language models (LLMs) to adapt to downstream tasks efficiently. Federated …
Examining the revolutionary effects of AI-powered tools in the field of contract analysis and management for legal document inspection is the focus of this study. …
The legal sector often faces delays and inefficiencies due to the overwhelming volume of information, the labor-intensive nature of research, and high service costs. This …
Abstract This article addresses the legal tensions between artificial intelligence (AI) development and copyright law, exploring policymaking on the use of copyrighted data for AI …
