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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 4411–4420 of 9051 papers

TitleStatusHype
Benchmarking Large Language Models with Augmented Instructions for Fine-grained Information Extraction—0
DFS: A Diverse Feature Synthesis Model for Generalized Zero-Shot Learning—0
An Analysis of Generative Methods for Multiple Image Inpainting—0
2-D Coherence Factor for Sidelobe and Ghost Suppressions in Radar Imaging—0
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning—0
Benchmarking General-Purpose In-Context Learning—0
Achieving Diversity in Counterfactual Explanations: a Review and Discussion—0
DFlow: Diverse Dialogue Flow Simulation with Large Language Models—0
DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification—0
Benchmarking foundation models as feature extractors for weakly-supervised computational pathology—0
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