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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 33913400 of 9051 papers

TitleStatusHype
Activitynet 2019 Task 3: Exploring Contexts for Dense Captioning Events in Videos0
Abstractive and mixed summarization for long-single documents0
Evaluations at Work: Measuring the Capabilities of GenAI in Use0
A Tailored NSGA-III Instantiation for Flexible Job Shop Scheduling0
ControlVAE: Controllable Variational Autoencoder0
A Systematic Survey on Deep Generative Models for Graph Generation0
A K-means-based Multi-subpopulation Particle Swarm Optimization for Neural Network Ensemble0
Controllable Text Generation with Focused Variation0
Controllable Satellite-to-Street-View Synthesis with Precise Pose Alignment and Zero-Shot Environmental Control0
A Systematic Exploration of Diversity in Machine Translation0
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