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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 33913400 of 9051 papers

TitleStatusHype
VoxArabica: A Robust Dialect-Aware Arabic Speech Recognition System0
Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search DecodingCode0
An Empirical Study of Translation Hypothesis Ensembling with Large Language ModelsCode0
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework DesignCode1
ACES: Generating Diverse Programming Puzzles with with Autotelic Generative Models0
CoCoFormer: A controllable feature-rich polyphonic music generation methodCode0
SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack0
Diversifying the Mixture-of-Experts Representation for Language Models with Orthogonal Optimizer0
Private Synthetic Data Meets Ensemble Learning0
Graph Neural Network approaches for single-cell data: A recent overview0
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