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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 8751–8775 of 9051 papers

TitleStatusHype
Diversified Visual Attention Networks for Fine-Grained Object Classification—0
Characterization of Methicillin-resistant Staphylococcus aureus Isolates from Fitness Centers in Memphis Metropolitan Area, USA—0
PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach—0
Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization—0
Kernel-based Generative Learning in Distortion Feature Space—0
An Empirical Comparison of Sampling Quality Metrics: A Case Study for Bayesian Nonnegative Matrix Factorization—0
Adapting ELM to Time Series Classification: A Novel Diversified Top-k Shapelets Extraction Method—0
Query-Focused Opinion Summarization for User-Generated Content—0
Smart Reply: Automated Response Suggestion for Email—0
Deep Reinforcement Learning for Dialogue GenerationCode0
On Benefits of Selection Diversity via Bilevel Exclusive Sparsity—0
Online Detection and Classification of Dynamic Hand Gestures With Recurrent 3D Convolutional Neural Network—0
Samsung Poland NLP Team at SemEval-2016 Task 1: Necessity for diversity; combining recursive autoencoders, WordNet and ensemble methods to measure semantic similarity.—0
Communities as cliques—0
Evolutionary Demographic Algorithms—0
A model for the clustered distribution of SNPs in the human genome—0
Mitochondrial genealogy of Maria Mercedes Cairol Antunez, footprint of recent immigration to Costa Rica / La genealogia mitocondrial de Maria Mercedes Cairol Antunez, huella de la inmigracion reciente a Costa Rica—0
Automatic Detection and Categorization of Election-Related Tweets—0
Locally Weighted Ensemble Clustering—0
Human Action Localization with Sparse Spatial Supervision—0
Diversity of emergent dynamics in competitive threshold-linear networksCode0
Wisdom of Crowds cluster ensemble—0
When Do Luxury Cars Hit the Road? Findings by A Big Data Approach—0
Adversarial Diversity and Hard Positive Generation—0
The ACQDIV Database: Min(d)ing the Ambient Language—0
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