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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 681690 of 9051 papers

TitleStatusHype
EmpHi: Generating Empathetic Responses with Human-like IntentsCode1
Diversified Batch Selection for Training AccelerationCode1
Can pre-trained models assist in dataset distillation?Code1
CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource LanguagesCode1
Can we use Common Voice to train a Multi-Speaker TTS system?Code1
Diversify and Conquer: Diversity-Centric Data Selection with Iterative RefinementCode1
Advancing Fine-Grained Classification by Structure and Subject Preserving AugmentationCode1
Data Curation Alone Can Stabilize In-context LearningCode1
ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative GenerationCode1
Diversifying Dialog Generation via Adaptive Label SmoothingCode1
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