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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 521530 of 9051 papers

TitleStatusHype
Learning to Transform Dynamically for Better Adversarial TransferabilityCode1
Annotation-Efficient Preference Optimization for Language Model AlignmentCode1
DirectMultiStep: Direct Route Generation for Multi-Step RetrosynthesisCode1
Mosaic-IT: Free Compositional Data Augmentation Improves Instruction TuningCode1
Addressing the Elephant in the Room: Robust Animal Re-Identification with Unsupervised Part-Based Feature AlignmentCode1
G-DIG: Towards Gradient-based Diverse and High-quality Instruction Data Selection for Machine TranslationCode1
Goals as Reward-Producing ProgramsCode1
PT43D: A Probabilistic Transformer for Generating 3D Shapes from Single Highly-Ambiguous RGB ImagesCode1
SynthesizRR: Generating Diverse Datasets with Retrieval AugmentationCode1
Color Space Learning for Cross-Color Person Re-IdentificationCode1
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