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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 38613870 of 9051 papers

TitleStatusHype
Galaxy Image Simulation Using Progressive GANs0
Gradient Diversity: a Key Ingredient for Scalable Distributed Learning0
GAMA++: Disentangled Geometric Alignment with Adaptive Contrastive Perturbation for Reliable Domain Transfer0
Audio-to-Image Cross-Modal Generation0
NashFormer: Leveraging Local Nash Equilibria for Semantically Diverse Trajectory Prediction0
GameVibe: A Multimodal Affective Game Corpus0
Gamification Platform for Collecting Task-oriented Dialogue Data0
GAMMT: Generative Ambiguity Modeling Using Multiple Transformers0
GAN based ball screw drive picture database enlargement for failure classification0
Gradient-Informed Quality Diversity for the Illumination of Discrete Spaces0
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