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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 15311540 of 9051 papers

TitleStatusHype
Inducing High Energy-Latency of Large Vision-Language Models with Verbose ImagesCode1
Semantic Generative Augmentations for Few-Shot CountingCode1
SemEval-2023 Task 10: Explainable Detection of Online SexismCode1
Clotho: An Audio Captioning DatasetCode1
Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse GranularityCode1
IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity RecognitionCode1
CloudEval-YAML: A Practical Benchmark for Cloud Configuration GenerationCode1
A Map of Diverse Synthetic Stable Roommates InstancesCode1
FALL-E: A Foley Sound Synthesis Model and StrategiesCode1
IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion ModelsCode1
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