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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 6801–6825 of 9051 papers

TitleStatusHype
Quality-diversity for aesthetic evolution—0
Annotating Cognates and Etymological Origin in Turkic Languages—0
Quality Diversity for Robot Learning: Limitations and Future Directions—0
Quality Diversity for Visual Pre-Training—0
An LLM-Empowered Adaptive Evolutionary Algorithm For Multi-Component Deep Learning Systems—0
Quality Diversity Imitation Learning—0
Quality-diversity in dissimilarity spaces—0
Quality Diversity in the Amorphous Fortress (QD-AF): Evolving for Complexity in 0-Player Games—0
Unsupervised and Supervised Structure Learning for Protein Contact Prediction—0
Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning—0
An Investigation of the (In)effectiveness of Counterfactually Augmented Data—0
Quality-Diversity Red-Teaming: Automated Generation of High-Quality and Diverse Attackers for Large Language Models—0
Quality-Diversity through AI Feedback—0
An Investigation of Hybrid architectures for Low Resource Multilingual Speech Recognition system in Indian context—0
Absolute Ranking: An Essential Normalization for Benchmarking Optimization Algorithms—0
An investigation into language complexity of World-of-Warcraft game-external texts—0
Why the Mansfield Rule can't work: a supply demand analysis—0
Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning—0
Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing—0
Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation—0
Unsupervised Data Augmentation for Less-Resourced Languages with no Standardized Spelling—0
Quality with Just Enough Diversity in Evolutionary Policy Search—0
Quantification of the effect of mutations using a global probability model of natural sequence variation—0
Quantifying and Reducing Speaker Heterogeneity within the Common Voice Corpus for Phonetic Analysis—0
Quantifying barley morphology using the Euler characteristic transform—0
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