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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 32213230 of 9051 papers

TitleStatusHype
CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation0
A unified framework based on graph consensus term for multi-view learning0
CTSyn: A Foundational Model for Cross Tabular Data Generation0
CT-SGAN: Computed Tomography Synthesis GAN0
CtrTab: Tabular Data Synthesis with High-Dimensional and Limited Data0
AugRefer: Advancing 3D Visual Grounding via Cross-Modal Augmentation and Spatial Relation-based Referring0
Allowing for equal opportunities for artists in music recommendation0
All or None: Identifiable Linear Properties of Next-token Predictors in Language Modeling0
Accelerated DC loadflow solver for topology optimization0
HelpSteer3-Preference: Open Human-Annotated Preference Data across Diverse Tasks and Languages0
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