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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

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Showing 821–830 of 9051 papers

TitleStatusHype
Embedding-Driven Diversity Sampling to Improve Few-Shot Synthetic Data Generation—0
Are generative models fair? A study of racial bias in dermatological image generation—0
Causal Learning for Heterogeneous Subgroups Based on Nonlinear Causal Kernel Clustering—0
A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANsCode4
Curiosity-Driven Reinforcement Learning from Human FeedbackCode1
Generative AI and Large Language Models in Language Preservation: Opportunities and Challenges—0
Optimizing Pretraining Data Mixtures with LLM-Estimated Utility—0
Block Flow: Learning Straight Flow on Data BlocksCode0
GVMGen: A General Video-to-Music Generation Model with Hierarchical Attentions—0
FoundationStereo: Zero-Shot Stereo MatchingCode7
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