Zobrazeno 1 - 10
of 572
pro vyhledávání: '"H.3.4"'
Enterprise searches require users to have complex knowledge of queries, configurations, and metadata, rendering it difficult for them to access information as needed. Most go-to-market (GTM) platforms utilize advanced search, an interface that enable
Externí odkaz:
http://arxiv.org/abs/2411.05048
Autor:
Wertenbroek, Rick, Dassatti, Alberto
Modern operating systems manage and abstract hardware resources, to ensure efficient execution of user workloads. The operating system must securely interface with often untrusted user code while relying on hardware that is assumed to be trustworthy.
Externí odkaz:
http://arxiv.org/abs/2411.00439
Federated Learning (FL) facilitates data privacy by enabling collaborative in-situ training across decentralized clients. Despite its inherent advantages, FL faces significant challenges of performance and convergence when dealing with data that is n
Externí odkaz:
http://arxiv.org/abs/2410.03499
Autor:
Olasunkanmi, Olawumi, Morris, Evan, Kebede, Yaphet, Lee, Harlin, Ahalt, Stanley, Tropsha, Alexander, Bizon, Chris
Knowledge graphs (KGs) represent connections and relationships between real-world entities. We propose a link prediction framework for KGs named Enrichment-Driven GrAph Reasoner (EDGAR), which infers new edges by mining entity-local rules. This appro
Externí odkaz:
http://arxiv.org/abs/2409.18659
Federated learning faces a critical challenge in balancing communication efficiency with rapid convergence, especially for second-order methods. While Newton-type algorithms achieve linear convergence in communication rounds, transmitting full Hessia
Externí odkaz:
http://arxiv.org/abs/2409.15216
Autor:
Corsi, Matteo, Urbano, Julián
Rank-Biased Overlap (RBO) is a similarity measure for indefinite rankings: it is top-weighted, and can be computed when only a prefix of the rankings is known or when they have only some items in common. It is widely used for instance to analyze diff
Externí odkaz:
http://arxiv.org/abs/2406.07121
Calibration in recommender systems is an important performance criterion that ensures consistency between the distribution of user preference categories and that of recommendations generated by the system. Standard methods for mitigating miscalibrati
Externí odkaz:
http://arxiv.org/abs/2405.10232
Autor:
Baumann, Oliver, Nandini, Durgesh, Rossanez, Anderson, Schoenfeld, Mirco, Reis, Julio Cesar dos
Traditional recommendation proposals, including content-based and collaborative filtering, usually focus on similarity between items or users. Existing approaches lack ways of introducing unexpectedness into recommendations, prioritizing globally pop
Externí odkaz:
http://arxiv.org/abs/2405.08465
High-Performance Computing (HPC) systems excel in managing distributed workloads, and the growing interest in Artificial Intelligence (AI) has resulted in a surge in demand for faster methods of Machine Learning (ML) model training and inference. In
Externí odkaz:
http://arxiv.org/abs/2404.10386
We present SocialGenPod, a decentralised and privacy-friendly way of deploying generative AI Web applications. Unlike centralised Web and data architectures that keep user data tied to application and service providers, we show how one can use Solid
Externí odkaz:
http://arxiv.org/abs/2403.10408