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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 6326–6350 of 9051 papers

TitleStatusHype
FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs Only—0
Pairwise Instance Relation Augmentation for Long-tailed Multi-label Text Classification—0
Coreference Resolution: Are the eliminated spans totally worthless?—0
Pal-GAN: Palette-conditioned Generative Adversarial Networks—0
PALMAR: Towards Adaptive Multi-inhabitant Activity Recognition in Point-Cloud Technology—0
Palm-GAN: Generating Realistic Palmprint Images Using Total-Variation Regularized GAN—0
PanLex: Building a Resource for Panlingual Lexical Translation—0
PanoMixSwap Panorama Mixing via Structural Swapping for Indoor Scene Understanding—0
Are Easy Data Easy (for K-Means)—0
Pan-tropical plant functional trait variation from space—0
Tailoring Graph Neural Network-based Flow-guided Localization to Individual Bloodstreams and Activities—0
Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle—0
ParaBank: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-constrained Neural Machine Translation—0
ParaFusion: A Large-Scale LLM-Driven English Paraphrase Dataset Infused with High-Quality Lexical and Syntactic Diversity—0
Paragraph-based complex networks: application to document classification and authenticity verification—0
Parallelizing Contextual Bandits—0
Parallel Optimal Transport GAN—0
A Reduction Approach to Constrained Reinforcement Learning—0
Understanding the Limitations of Diffusion Concept Algebra Through Food—0
Parameter Efficient Diverse Paraphrase Generation Using Sequence-Level Knowledge Distillation—0
Parameter-efficient Dysarthric Speech Recognition Using Adapter Fusion and Householder Transformation—0
Parameter estimation in FACS-seq enables high-throughput characterization of phenotypic heterogeneity—0
Parameterized Analysis of Multi-objective Evolutionary Algorithms and the Weighted Vertex Cover Problem—0
Parameterized Compilation Lower Bounds for Restricted CNF-formulas—0
Parameterized Synthetic Image Data Set for Fisheye Lens—0
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