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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 47014710 of 9051 papers

TitleStatusHype
Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks0
LaMAR: Benchmarking Localization and Mapping for Augmented RealityCode2
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models0
Rethinking Prototypical Contrastive Learning through Alignment, Uniformity and Correlation0
Optimizing Hierarchical Image VAEs for Sample QualityCode1
DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text GenerationCode1
Intra-Source Style Augmentation for Improved Domain GeneralizationCode1
Online Damage Recovery for Physical Robots with Hierarchical Quality-DiversityCode1
Measures of Information Reflect Memorization Patterns0
Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent DiscoveryCode0
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