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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 19611970 of 9051 papers

TitleStatusHype
Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder0
MST5 -- Multilingual Question Answering over Knowledge GraphsCode0
Invariance Principle Meets Vicinal Risk Minimization0
A Factuality and Diversity Reconciled Decoding Method for Knowledge-Grounded Dialogue Generation0
3D Vision and Language Pretraining with Large-Scale Synthetic DataCode1
Sub-SA: Strengthen In-context Learning via Submodular Selective AnnotationCode0
Training Task Experts through Retrieval Based Distillation0
UltraEdit: Instruction-based Fine-Grained Image Editing at ScaleCode0
Harmony in Diversity: Merging Neural Networks with Canonical Correlation AnalysisCode0
Multi-scale Conditional Generative Modeling for Microscopic Image Restoration0
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