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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 6026–6050 of 9051 papers

TitleStatusHype
Adaptable Agent Populations via a Generative Model of PoliciesCode1
Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiersCode1
A Deep Generative Artificial Intelligence system to decipher species coexistence patternsCode0
Statistics of the number of equilibria in random social dilemma evolutionary games with mutation—0
eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition ChallengesCode1
Fast Batch Nuclear-norm Maximization and Minimization for Robust Domain AdaptationCode1
This Person (Probably) Exists. Identity Membership Attacks Against GAN Generated Faces—0
Prb-GAN: A Probabilistic Framework for GAN Modelling—0
Sparse Personalized Federated LearningCode0
CatVRNN: Generating Category Texts via Multi-task LearningCode0
A Simple Reward-free Approach to Constrained Reinforcement Learning—0
Sliding Spectrum Decomposition for Diversified Recommendation—0
Asymptotic analysis of V-BLAST MIMO for coherent optical wireless communications in Gamma-Gamma turbulence—0
Designing Recommender Systems to Depolarize—0
Self-Referential Quality Diversity Through Differential Map-Elites—0
HOMRS: High Order Metamorphic Relations Selector for Deep Neural Networks—0
Semi-Supervised Learning with Multi-Head Co-TrainingCode1
Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem—0
GGT: Graph-Guided Testing for Adversarial Sample Detection of Deep Neural Network—0
Wavelet Transform-assisted Adaptive Generative Modeling for ColorizationCode1
Multi-headed Neural Ensemble Search—0
Diverse Video Generation using a Gaussian Process TriggerCode1
Selective Focusing Learning in Conditional GANs—0
Ensembles of Randomized NNs for Pattern-based Time Series Forecasting—0
EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli ClassificationCode1
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