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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 3661–3670 of 9051 papers

TitleStatusHype
iTool: Boosting Tool Use of Large Language Models via Iterative Reinforced Fine-Tuning—0
Diverse Video Captioning Through Latent Variable Expansion—0
Boosting the Transferability of Adversarial Examples via Local Mixup and Adaptive Step Size—0
Feint Behaviors and Strategies: Formalization, Implementation and Evaluation—0
An Improved Grey Wolf Optimization Algorithm for Heart Disease Prediction—0
Ferret-UI 2: Mastering Universal User Interface Understanding Across Platforms—0
Few-shot 3D Shape Generation—0
Few-Shot Airway-Tree Modeling using Data-Driven Sparse Priors—0
Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators—0
A document processing pipeline for the construction of a dataset for topic modeling based on the judgments of the Italian Supreme Court—0
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