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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 1971–1980 of 9051 papers

TitleStatusHype
The Solution for Language-Enhanced Image New Category Discovery—0
Progress or Regress? Self-Improvement Reversal in Post-training—0
VRSD: Rethinking Similarity and Diversity for Retrieval in Large Language Models—0
Few-Shot Airway-Tree Modeling using Data-Driven Sparse Priors—0
Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling—0
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
DiffRetouch: Using Diffusion to Retouch on the Shoulder of Experts—0
FDS: Feedback-guided Domain Synthesis with Multi-Source Conditional Diffusion Models for Domain GeneralizationCode1
A Survey of Data Synthesis ApproachesCode0
On the Effectiveness of Acoustic BPE in Decoder-Only TTS—0
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