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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

Showing 25712580 of 9051 papers

TitleStatusHype
Diverse Image Captioning with Grounded StyleCode0
Adder Attention for Vision TransformerCode0
Improving Screening Processes via Calibrated Subset SelectionCode0
Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field SamplingCode0
Benchmarking histopathology foundation models in a multi-center dataset for skin cancer subtypingCode0
Flow-Grounded Spatial-Temporal Video Prediction from Still ImagesCode0
BOLD5000: A public fMRI dataset of 5000 imagesCode0
Improving Transferability of Adversarial Examples with Input DiversityCode0
DFPE: A Diverse Fingerprint Ensemble for Enhancing LLM PerformanceCode0
Flexible Modeling of Diversity with Strongly Log-Concave DistributionsCode0
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