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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
Controllable Video Captioning with an Exemplar SentenceCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
An Empirical Investigation of Pre-Trained Transformer Language Models for Open-Domain Dialogue GenerationCode1
Annotation-Efficient Preference Optimization for Language Model AlignmentCode1
DiffuSum: Generation Enhanced Extractive Summarization with DiffusionCode1
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic DiversityCode1
Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier ExamplesCode1
Controllable Open-ended Question Generation with A New Question Type OntologyCode1
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
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