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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 8251–8275 of 9051 papers

TitleStatusHype
Prototype Discovery using Quality-Diversity—0
Big data analyses reveal patterns and drivers of the movements of southern elephant seals—0
Examining Scientific Writing Styles from the Perspective of Linguistic Complexity—0
3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single ImageCode0
User Fairness in Recommender Systems—0
Knowledge-aware Autoencoders for Explainable Recommender Sytems—0
Entropy and Energy Detection-based Spectrum Sensing over F Composite Fading Channels—0
Analysis and Modeling of Behavioral Changes in a News Service—0
Assessing fish abundance from underwater video using deep neural networks—0
Efficiently Secure Broadcasting in 5G Wireless Fog-Based-Fronthaul Networks—0
Computing recommendations via a Knowledge Graph-aware AutoencoderCode0
A Many-Objective Evolutionary Algorithm Based on Decomposition and Local Dominance—0
Instance-based entropy fuzzy support vector machine for imbalanced data—0
Variational Capsules for Image Analysis and Synthesis—0
How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization—0
A Combined CNN and LSTM Model for Arabic Sentiment Analysis—0
Quality Diversity Through SurpriseCode0
Curiosity Driven Exploration of Learned Disentangled Goal SpacesCode0
Sequence-to-Sequence Data Augmentation for Dialogue Language UnderstandingCode0
Diversity in Machine Learning—0
Stochastic Channel Decorrelation Network and Its Application to Visual Tracking—0
Deep Architectures and Ensembles for Semantic Video Classification—0
Dynamic Swarm Dispersion in Particle Swarm Optimization for Mining Unsearched Area in Solution Space (DSDPSO)—0
Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy—0
Constrained Interacting Submodular Groupings—0
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