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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 13811390 of 9051 papers

TitleStatusHype
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
Open Source Automatic Speech Recognition for GermanCode1
Hierarchical Sketch Induction for Paraphrase GenerationCode1
OPFython: A Python-Inspired Optimum-Path Forest ClassifierCode1
Diversity-Guided Multi-Objective Bayesian Optimization With Batch EvaluationsCode1
Diversity-Guided MLP Reduction for Efficient Large Vision TransformersCode1
Can 3D Vision-Language Models Truly Understand Natural Language?Code1
Diversity is All You Need: Learning Skills without a Reward FunctionCode1
Diversity-Measurable Anomaly DetectionCode1
HausaMT v1.0: Towards English--Hausa Neural Machine TranslationCode1
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