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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 48614870 of 9051 papers

TitleStatusHype
MDIA: A Benchmark for Multilingual Dialogue Generation in 46 LanguagesCode0
Nitrogen-induced hysteresis in grassland biodiversity: a theoretical test of litter-mediated mechanisms0
BITS: Bi-level Imitation for Traffic SimulationCode2
Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages0
A Block-Based Adaptive Decoupling Framework for Graph Neural NetworksCode0
Multimedia Generative Script Learning for Task PlanningCode0
Alleviating Search Bias in Bayesian Evolutionary Optimization with Many Heterogeneous Objectives0
Fundamentals of Task-Agnostic Data Valuation0
PEER: A Collaborative Language Model0
GAN-based generative modelling for dermatological applications -- comparative studyCode1
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