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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 8401–8450 of 9051 papers

TitleStatusHype
WebSeg: Learning Semantic Segmentation from Web Searches—0
Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification—0
On the Runtime Analysis of the Clearing Diversity-Preserving Mechanism—0
Runtime Analysis of Probabilistic Crowding and Restricted Tournament Selection for Bimodal Optimisation—0
Unsupervised Separation of Transliterable and Native Words for Malayalam—0
A trust-based recommendation method using network diffusion processes—0
Use of Genome Information-Based Potentials to Characterize Human Adaptation—0
A Low Complexity Space-Frequency Multiuser Scheduling Algorithm—0
Advanced Signal Processing Techniques for Fixed and Mobile Satellite Communications—0
Improving Transferability of Adversarial Examples with Input DiversityCode0
Neural Text Generation: Past, Present and Beyond—0
A Novel Detection Algorithm Efficient for Turbo coded CDMA Signals in Detect and Forward Cooperative Channels—0
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
Evolutionary Generative Adversarial NetworksCode0
Inferring Missing Categorical Information in Noisy and Sparse Web Markup—0
Diversity and degrees of freedom in regression ensembles—0
Speciation in a MacArthur model predicts growth, stability and adaptation in ecosystems dynamics—0
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy MethodsCode0
Analyzing Uncertainty in Neural Machine TranslationCode0
IGD Indicator-based Evolutionary Algorithm for Many-objective Optimization Problems—0
Improved Regularity Model-based EDA for Many-objective Optimization—0
BigDataBench: A Scalable and Unified Big Data and AI Benchmark Suite—0
Exact Sampling of Determinantal Point Processes without Eigendecomposition—0
Diverse Exploration for Fast and Safe Policy Improvement—0
Diversity regularization in deep ensembles—0
Global-scale phylogenetic linguistic inference from lexical resources—0
Phonemic evidence reveals interwoven evolution of Chinese dialects—0
Discrepancy-based Evolutionary Diversity Optimization—0
Learning Determinantal Point Processes by Corrective Negative Sampling—0
Evolution of Images with Diversity and Constraints Using a Generator Network—0
Molecular Structure Extraction From Documents Using Deep Learning—0
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning AlgorithmsCode0
Superposition-Assisted Stochastic Optimization for Hawkes Processes—0
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning—0
Predicting Adversarial Examples with High Confidence—0
Fair and Diverse DPP-based Data SummarizationCode0
TVM: An Automated End-to-End Optimizing Compiler for Deep LearningCode0
A Contextual Bandit Bake-offCode0
ClosNets: a Priori Sparse Topologies for Faster DNN Training—0
A 1Mbps Real-time NLOS UV Scattering Communication System with Receiver Diversity over 1km—0
MOEA/D with Angle-based Constrained Dominance Principle for Constrained Multi-objective Optimization Problems—0
Coordinated Exploration in Concurrent Reinforcement Learning—0
Diverse Beam Search for Increased Novelty in Abstractive Summarization—0
DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified TextCode0
Bayesian Renewables Scenario Generation via Deep Generative NetworksCode0
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