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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 4951–4975 of 9051 papers

TitleStatusHype
TIGTEC : Token Importance Guided TExt Counterfactuals—0
Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning—0
Semi-Supervised Semantic Segmentation With Region RelevanceCode0
SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models—0
Constructing a meta-learner for unsupervised anomaly detection—0
Quantifying the difference between phylogenetic diversity and diversity indices—0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Domain Generalization for Mammographic Image Analysis with Contrastive Learning—0
HyperTuner: A Cross-Layer Multi-Objective Hyperparameter Auto-Tuning Framework for Data Analytic ServicesCode0
SP-BatikGAN: An Efficient Generative Adversarial Network for Symmetric Pattern Generation—0
ReelFramer: Human-AI Co-Creation for News-to-Video Translation—0
Learning Representative Trajectories of Dynamical Systems via Domain-Adaptive ImitationCode0
Analysing Equilibrium States for Population Diversity—0
Dual Stage Stylization Modulation for Domain Generalized Semantic Segmentation—0
TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image ModelsCode0
Participatory Design of AI with Children: Reflections on IDC Design Challenge—0
Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction—0
The MiniPile Challenge for Data-Efficient Language ModelsCode0
CAViaR: Context Aware Video Recommendations—0
Do you MIND? Reflections on the MIND dataset for research on diversity in news recommendations—0
ERTIM@MC2: Diversified Argumentative Tweets Retrieval—0
Test-Optional Admissions—0
Context-aware Domain Adaptation for Time Series Anomaly Detection—0
Text-Conditional Contextualized Avatars For Zero-Shot Personalization—0
The Second Monocular Depth Estimation Challenge—0
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