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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 9911000 of 9051 papers

TitleStatusHype
AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language ModelsCode1
AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task GenerationCode1
DSDL: Data Set Description Language for Bridging Modalities and Tasks in AI DataCode1
Dual Feature Augmentation Network for Generalized Zero-shot LearningCode1
ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-Tree Complex Wavelet Representation and Contradict Channel LossCode1
Learning a Cross-modality Anomaly Detector for Remote Sensing ImageryCode1
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving PlannersCode1
FALL-E: A Foley Sound Synthesis Model and StrategiesCode1
FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMsCode1
FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANsCode1
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