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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 4726–4750 of 9051 papers

TitleStatusHype
Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data—0
Towards Advanced Mathematical Reasoning for LLMs via First-Order Logic Theorem Proving—0
Intrinsic Image Captioning Evaluation—0
Intrinsic meaning, perception, and matching—0
Introducing Coherent MIMO Sensing, a fading-resilient, polarization-independent approach to phase-OTDR—0
Introducing MULAI: A Multimodal Database of Laughter during Dyadic Interactions—0
Introducing SDICE: An Index for Assessing Diversity of Synthetic Medical Datasets—0
Introducing the CLARIN Knowledge Centre for Linguistic Diversity and Language Documentation—0
Introducing the diagrammatic semiotic mode—0
Introduction of a novel word embedding approach based on technology labels extracted from patent data—0
Introduction to the Artificial Intelligence that can be applied to the Network Automation Journey—0
Intuition-aware Mixture-of-Rank-1-Experts for Parameter Efficient Finetuning—0
Beyond Relevance: An Adaptive Exploration-Based Framework for Personalized Recommendations—0
Invasion and Interaction Determine Population Composition in an Open Evolving System—0
Beyond Recommender: An Exploratory Study of the Effects of Different AI Roles in AI-Assisted Decision Making—0
Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation—0
Inverse problems with experiment-guided AlphaFold—0
Graph Inverse Reinforcement Learning from Diverse Videos—0
Inverse Relationship Between Molecular Diversity And Resource Abundances—0
Investigating a Benchmark for Training-set free Evaluation of Linguistic Capabilities in Machine Reading Comprehension—0
Investigating Bias in Deep Face Analysis: The KANFace Dataset and Empirical Study—0
Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Go—0
Towards A Generalist Code Embedding Model Based On Massive Data Synthesis—0
Investigating Shifts in GAN Output-Distributions—0
Investigating the effects Diversity Mechanisms have on Evolutionary Algorithms in Dynamic Environments—0
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