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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 711720 of 9051 papers

TitleStatusHype
3D Vision and Language Pretraining with Large-Scale Synthetic DataCode1
Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided DiffusionCode1
DREAM: Efficient Dataset Distillation by Representative MatchingCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT ImagesCode1
DSDL: Data Set Description Language for Bridging Modalities and Tasks in AI DataCode1
Adversarial Feature Hallucination Networks for Few-Shot LearningCode1
Curriculum-guided Hindsight Experience ReplayCode1
DeepFacePencil: Creating Face Images from Freehand SketchesCode1
Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code ContributionsCode1
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