arXiv:2310. 11714v5 Announce Type: replace Abstract: Ranking generative models based on the fidelity and diversity of their outputs is required to identify the best generator in a group of candidate generative AI models.
arXiv:2608. 08422v1 Announce Type: cross Abstract: Ranking data arise in scientific and machine learning applications, including recommendation systems, information retrieval, voting, marketing, and AI preference ranking from human feedback.
arXiv:2602. 11453v2 Announce Type: replace-cross Abstract: In information retrieval (IR), learning-to-rank (LTR) methods have traditionally limited themselves to discriminative machine learning approaches that model the probability of the document being relevant to the query given some feature representation of the query-document pair.
arXiv:2604. 05324v2 Announce Type: replace Abstract: Statistical evaluation aims to estimate the generalization performance of a model using held-out i.
arXiv:2608. 01301v2 Announce Type: replace-cross Abstract: Infrared-visible image fusion (IVIF) has no ideal fused reference, so fusion algorithms are routinely ranked by scalar objective metrics that formalize different proxies for information transfer, structure, or source similarity.
arXiv:2608. 17731v1 Announce Type: new Abstract: Diversity is a fundamental criterion for evaluating generative artificial intelligence (AI) systems, yet its measurement remains inherently ambiguous.
arXiv:2607. 05046v1 Announce Type: new Abstract: Evaluating generative AI models is a routine, but resource-intensive, process that is conducted over and over again during the course of model development.
arXiv:2501. 18897v4 Announce Type: replace-cross Abstract: Generative models have achieved remarkable success across a range of applications, yet their evaluation still lacks principled uncertainty quantification.
arXiv:2606. 07492v1 Announce Type: cross Abstract: The ranking of recommendation algorithms is a challenging problem since model performance is sensitive to dataset characteristics such as sparsity, sequential structure, and scale.
arXiv:2608. 01301v3 Announce Type: replace-cross Abstract: Infrared-visible image fusion (IVIF) has no ideal fused reference, so algorithms are ranked by scalar objective metrics that formalize proxies for information transfer, structure, or source similarity.
arXiv:2511. 08307v2 Announce Type: replace-cross Abstract: Generative models, such as large language models or text-to-image diffusion models, can generate relevant responses to user-given queries.
arXiv:2606. 26899v1 Announce Type: new Abstract: Embedding-based retrieval ranks items by their similarity to a query in a shared vector space and usually aims to return the highest-scoring items.
arXiv:2606. 08921v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to predict missing facts from an observed knowledge graph (KG), playing a crucial role in a wide range of real-world applications such as drug discovery, recommender systems, and retrieval-augmented generation (RAG).