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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 4501–4525 of 9051 papers

TitleStatusHype
Beauty beacon: correlated strategies for the Fisher runaway process—0
Stochastic Region Pooling: Make Attention More Expressive—0
Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval—0
Dense Video Captioning: A Survey of Techniques, Datasets and Evaluation Protocols—0
Be a Multitude to Itself: A Prompt Evolution Framework for Red Teaming—0
Beamforming for Movable and Rotatable Antenna Enabled Multi-User Communications—0
Analysis of Quality Diversity Algorithms for the Knapsack Problem—0
Denoising Diffusion Probabilistic Models for Styled Walking Synthesis—0
Denoising Attention for Query-aware User Modeling in Personalized Search—0
Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity,Representation, Coverage and Importance—0
Demonstration Selection for In-Context Learning via Reinforcement Learning—0
Bayesian support for Evolution: detecting phylogenetic signal in a subset of the primate family—0
Analysis of Knowledge Tracing performance on synthesised student data—0
Demographic Attributes Prediction from Speech Using WavLM Embeddings—0
Democratizing AI Governance: Balancing Expertise and Public Participation—0
Interpretable Diversity Analysis: Visualizing Feature Representations In Low-Cost Ensembles—0
Analysis of genetic diversity among some Iraqi durum wheat cultivars revealed by different molecular markers—0
DEM: Distribution Edited Model for Training with Mixed Data Distributions—0
Interpretable Distance Metric Learning for Handwritten Chinese Character Recognition—0
Interpretable Learning Dynamics in Unsupervised Reinforcement Learning—0
Delving into Transformer for Incremental Semantic Segmentation—0
Delving into Rectifiers in Style-Based Image Translation—0
Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables—0
Analysis of function approximation and stability of general DNNs in directed acyclic graphs using un-rectifying analysis—0
Improving Limited Supervised Foot Ulcer Segmentation Using Cross-Domain Augmentation—0
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