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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 8426–8450 of 9051 papers

TitleStatusHype
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning AlgorithmsCode0
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning—0
Superposition-Assisted Stochastic Optimization for Hawkes Processes—0
Predicting Adversarial Examples with High Confidence—0
A 1Mbps Real-time NLOS UV Scattering Communication System with Receiver Diversity over 1km—0
Fair and Diverse DPP-based Data SummarizationCode0
ClosNets: a Priori Sparse Topologies for Faster DNN Training—0
TVM: An Automated End-to-End Optimizing Compiler for Deep LearningCode0
A Contextual Bandit Bake-offCode0
MOEA/D with Angle-based Constrained Dominance Principle for Constrained Multi-objective Optimization Problems—0
Texygen: A Benchmarking Platform for Text Generation ModelsCode1
Diverse Beam Search for Increased Novelty in Abstractive Summarization—0
Coordinated Exploration in Concurrent Reinforcement Learning—0
DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified TextCode0
Bayesian Renewables Scenario Generation via Deep Generative NetworksCode0
Bootstrapping and Multiple Imputation Ensemble Approaches for Missing DataCode0
Netizen-Style Commenting on Fashion Photos: Dataset and Diversity Measures—0
Improved Image Segmentation via Cost Minimization of Multiple HypothesesCode0
The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses—0
Benchmarking Clinical Decision Support Search—0
Improved Training of Generative Adversarial Networks Using Representative Features—0
FlashRL: A Reinforcement Learning Platform for Flash Games—0
Class label autoencoder for zero-shot learning—0
A mullti- or many- objective evolutionary algorithm with global loop update—0
Pruning Techniques for Mixed Ensembles of Genetic Programming ModelsCode0
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