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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 4141–4150 of 9051 papers

TitleStatusHype
BimArt: A Unified Approach for the Synthesis of 3D Bimanual Interaction with Articulated Objects—0
Distribution Aware Metrics for Conditional Natural Language Generation—0
A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning—0
Hybrid SD: Edge-Cloud Collaborative Inference for Stable Diffusion Models—0
HyperGANStrument: Instrument Sound Synthesis and Editing with Pitch-Invariant Hypernetworks—0
Distribution augmentation for low-resource expressive text-to-speech—0
Distribution Aligned Multimodal and Multi-Domain Image Stylization—0
Bilevel Scheduled Sampling for Dialogue Generation—0
Bi-level Mean Field: Dynamic Grouping for Large-Scale MARL—0
A Discrete CVAE for Response Generation on Short-Text Conversation—0
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