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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 4451–4475 of 9051 papers

TitleStatusHype
Detecting Sockpuppets in Deceptive Opinion Spam—0
Detecting Quality Problems in Data Models by Clustering Heterogeneous Data Values—0
Detecting patterns of species diversification in the presence of both rate shifts and mass extinctions—0
Being Considerate as a Pathway Towards Pluralistic Alignment for Agentic AI—0
Detecting Bone Lesions in X-Ray Under Diverse Acquisition Conditions—0
Detail-Preserving Latent Diffusion for Stable Shadow Removal—0
Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment—0
A Data Quality Assessment Framework for AI-enabled Wireless Communication—0
Instance-aware Remote Sensing Image Captioning with Cross-hierarchy Attention—0
Insular microbiogeography—0
Interactive Image Selection and Training for Brain Tumor Segmentation Network—0
Intrinsic meaning, perception, and matching—0
Iteratively Learn Diverse Strategies with State Distance Information—0
Behaviour Planning: A Toolkit for Diverse Planning—0
DesnowNet: Context-Aware Deep Network for Snow Removal—0
Behavioural Repertoire via Generative Adversarial Policy Networks—0
Market Design with Distributional Objectives—0
A database for face presentation attack using wax figure faces—0
Designing Recommender Systems to Depolarize—0
Designing Data: Proactive Data Collection and Iteration for Machine Learning—0
Behavioral Repertoires for Soft Tensegrity Robots—0
Designing a Robust Radiology Report Generation System—0
Behavioral Malware Classification using Convolutional Recurrent Neural Networks—0
Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective—0
InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions—0
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