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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 781790 of 9051 papers

TitleStatusHype
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
ARBERT & MARBERT: Deep Bidirectional Transformers for ArabicCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Controllable Video Captioning with an Exemplar SentenceCode1
Forecasting Future World Events with Neural NetworksCode1
Fork or Fail: Cycle-Consistent Training with Many-to-One MappingsCode1
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
Fractal Autoencoders for Feature SelectionCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
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