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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 1651–1675 of 9051 papers

TitleStatusHype
Alleviating Search Bias in Bayesian Evolutionary Optimization with Many Heterogeneous Objectives—0
AdaptaGen: Domain-Specific Image Generation through Hierarchical Semantic Optimization Framework—0
Characterizing Model Collapse in Large Language Models Using Semantic Networks and Next-Token Probability—0
Cultural Incongruencies in Artificial Intelligence—0
CURAJ_IIITDWD@LT-EDI-ACL 2022: Hope Speech Detection in English YouTube Comments using Deep Learning Techniques—0
Curatr: A Platform for Semantic Analysis and Curation of Historical Literary Texts—0
Augmented Message Passing Stein Variational Gradient Descent—0
Align Your Rhythm: Generating Highly Aligned Dance Poses with Gating-Enhanced Rhythm-Aware Feature Representation—0
Augmented Data Science: Towards Industrialization and Democratization of Data Science—0
Augmented Conditioning Is Enough For Effective Training Image Generation—0
Dialog State Tracking with Reinforced Data Augmentation—0
Augment, Drop & Swap: Improving Diversity in LLM Captions for Efficient Music-Text Representation Learning—0
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation—0
Adaptability of non-genetic diversity in bacterial chemotaxis—0
Augmentations vs Algorithms: What Works in Self-Supervised Learning—0
From Intent Discovery to Recognition with Topic Modeling and Synthetic Data—0
Cultural Evaluations of Vision-Language Models Have a Lot to Learn from Cultural Theory—0
Curiosity creates Diversity in Policy Search—0
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image—0
Aug2Search: Enhancing Facebook Marketplace Search with LLM-Generated Synthetic Data Augmentation—0
Auditing Source Diversity Bias in Video Search Results Using Virtual Agents—0
Auditing Google's Search Algorithm: Measuring News Diversity Across Brazil, the UK, and the US—0
Aligning Instruction Tuning with Pre-training—0
ADAGE: Active Defenses Against GNN Extraction—0
Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling—0
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