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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 8401–8425 of 9051 papers

TitleStatusHype
Enhanced Optimization with Composite Objectives and Novelty Selection—0
Generating Contradictory, Neutral, and Entailing Sentences—0
Comparison of various image fusion methods for impervious surface classification from VNREDSat-1—0
Less Is More: Picking Informative Frames for Video Captioning—0
A Many-Objective Evolutionary Algorithm With Two Interacting Processes: Cascade Clustering and Reference Point Incremental Learning—0
Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal ExplorationCode0
Speciation in a MacArthur model predicts growth, stability and adaptation in ecosystems dynamics—0
Inferring Missing Categorical Information in Noisy and Sparse Web Markup—0
Evolutionary Generative Adversarial NetworksCode0
Diversity and degrees of freedom in regression ensembles—0
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy MethodsCode0
Analyzing Uncertainty in Neural Machine TranslationCode0
Improved Regularity Model-based EDA for Many-objective Optimization—0
IGD Indicator-based Evolutionary Algorithm for Many-objective Optimization Problems—0
BigDataBench: A Scalable and Unified Big Data and AI Benchmark Suite—0
Exact Sampling of Determinantal Point Processes without Eigendecomposition—0
Diversity regularization in deep ensembles—0
Diverse Exploration for Fast and Safe Policy Improvement—0
Global-scale phylogenetic linguistic inference from lexical resources—0
Phonemic evidence reveals interwoven evolution of Chinese dialects—0
Diversity is All You Need: Learning Skills without a Reward FunctionCode1
Discrepancy-based Evolutionary Diversity Optimization—0
Evolution of Images with Diversity and Constraints Using a Generator Network—0
Learning Determinantal Point Processes by Corrective Negative Sampling—0
Molecular Structure Extraction From Documents Using Deep Learning—0
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