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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 671680 of 9051 papers

TitleStatusHype
An Empirical Study On Contrastive Search And Contrastive Decoding For Open-ended Text GenerationCode1
An Empirical Study of Vehicle Re-Identification on the AI City ChallengeCode1
C^2: Scalable Auto-Feedback for LLM-based Chart GenerationCode1
Calliar: An Online Handwritten Dataset for Arabic CalligraphyCode1
Annotation-Efficient Preference Optimization for Language Model AlignmentCode1
CamContextI2V: Context-aware Controllable Video GenerationCode1
Diverse Weight Averaging for Out-of-Distribution GeneralizationCode1
Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier ExamplesCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
BeLFusion: Latent Diffusion for Behavior-Driven Human Motion PredictionCode1
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