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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 11411150 of 9051 papers

TitleStatusHype
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
DiffuSum: Generation Enhanced Extractive Summarization with DiffusionCode1
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic DiversityCode1
DirectMultiStep: Direct Route Generation for Multi-Step RetrosynthesisCode1
DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text GenerationCode1
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
Analyzing Generalization of Vision and Language Navigation to Unseen Outdoor AreasCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
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