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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 13711380 of 9051 papers

TitleStatusHype
Harvesting Event Schemas from Large Language ModelsCode1
HausaMT v1.0: Towards English--Hausa Neural Machine TranslationCode1
On Disentangling Spoof Trace for Generic Face Anti-SpoofingCode1
Calliar: An Online Handwritten Dataset for Arabic CalligraphyCode1
CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model BiasCode1
Advancing Fine-Grained Classification by Structure and Subject Preserving AugmentationCode1
Online Task-Free Continual Generative and Discriminative Learning via Dynamic Cluster MemoryCode1
CamContextI2V: Context-aware Controllable Video GenerationCode1
ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative GenerationCode1
GS-Blur: A 3D Scene-Based Dataset for Realistic Image DeblurringCode1
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