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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 33813390 of 9051 papers

TitleStatusHype
Diverse Diffusion: Enhancing Image Diversity in Text-to-Image Generation0
AutoMix: Automatically Mixing Language ModelsCode1
MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error CorrectionCode1
Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven OptimizationCode1
PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly DetectionCode1
InfoDiffusion: Information Entropy Aware Diffusion Process for Non-Autoregressive Text GenerationCode0
DASA: Difficulty-Aware Semantic Augmentation for Speaker Verification0
Program Translation via Code Distillation0
VoxArabica: A Robust Dialect-Aware Arabic Speech Recognition System0
Keep Various Trajectories: Promoting Exploration of Ensemble Policies in Continuous Control0
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