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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 6826–6850 of 9051 papers

TitleStatusHype
Unsupervised Detection of Cancerous Regions in Histology Imagery using Image-to-Image Translation—0
Quantifying the effects of environment and population diversity in multi-agent reinforcement learning—0
Quantifying literature quality using complexity criteria—0
Why You Should Try the Real Data for the Scene Text Recognition—0
Quantifying Synthesis and Fusion and their Impact on Machine Translation—0
Quantifying Synthesis and Fusion and their Impact on Machine Translation—0
Quantifying the difference between phylogenetic diversity and diversity indices—0
Quantifying the Impact of Population Shift Across Age and Sex for Abdominal Organ Segmentation—0
Quantifying the Risks of Tool-assisted Rephrasing to Linguistic Diversity—0
Quantifying topological invariants of neuronal morphologies—0
Quantifying User Coherence: A Unified Framework for Cross-Domain Recommendation Analysis—0
An Interval-Based Bayesian Generative Model for Human Complex Activity Recognition—0
Unsupervised Ensemble Learning with Dependent Classifiers—0
Quantitative Assessment of DESIS Hyperspectral Data for Plant Biodiversity Estimation in Australia—0
Quantitative Comparison of Abundance Structures of Generalized Communities: From B-Cell Receptor Repertoires to Microbiomes—0
Quantitative Semantic Variation in the Contexts of Concrete and Abstract Words—0
Quantity versus Diversity: Influence of Data on Detecting EEG Pathology with Advanced ML Models—0
Quantum-Inspired Evolutionary Algorithms for Feature Subset Selection: A Comprehensive Survey—0
Quasi-random Multi-Sample Inference for Large Language Models—0
Unsupervised Features for Facial Expression Intensity Estimation over Time—0
Query Completion Using Bandits for Engines Aggregation—0
Query Expansion Using Contextual Clue Sampling with Language Models—0
Query-Focused Opinion Summarization for User-Generated Content—0
Question-Answering Based Summarization of Electronic Health Records using Retrieval Augmented Generation—0
An Interference-Free Filter-Bank Multicarrier System Applicable for MIMO Channels—0
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