SOTAVerified

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 2201–2225 of 9051 papers

TitleStatusHype
A comprehensive representation of selection at loci with multiple alleles that allows complex forms of genotypic fitnessCode0
Anti-Collapse Loss for Deep Metric Learning Based on Coding Rate MetricCode0
GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trickCode0
Adversarial Inference for Multi-Sentence Video DescriptionCode0
Growing Artificial Neural Networks for Control: the Role of Neuronal DiversityCode0
CausalDialogue: Modeling Utterance-level Causality in ConversationsCode0
CatVRNN: Generating Category Texts via Multi-task LearningCode0
Adversarial Imitation Learning with Trajectorial Augmentation and CorrectionCode0
Cats or CAT scans: transfer learning from natural or medical image source datasets?Code0
CatGAN: Category-aware Generative Adversarial Networks with Hierarchical Evolutionary Learning for Category Text GenerationCode0
Group Relative Policy Optimization for Image CaptioningCode0
Harnessing Distribution Ratio Estimators for Learning Agents with Quality and DiversityCode0
HiTR: Hierarchical Topic Model Re-estimation for Measuring Topical Diversity of DocumentsCode0
CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and CalibrationCode0
CAT: Contrastive Adapter Training for Personalized Image GenerationCode0
GridDehazeNet: Attention-Based Multi-Scale Network for Image DehazingCode0
Cascading CMA-ES Instances for Generating Input-diverse Solution BatchesCode0
GRATIS: GeneRAting TIme Series with diverse and controllable characteristicsCode0
A Comprehensive Evaluation on Event Reasoning of Large Language ModelsCode0
Gradient Estimators for Implicit ModelsCode0
Gram-Elites: N-Gram Based Quality-Diversity SearchCode0
Carbohydrate NMR chemical shift predictions using E(3) equivariant graph neural networksCode0
Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member ModelsCode0
Graph-guided Architecture Search for Real-time Semantic SegmentationCode0
Capturing the diversity of multilingual societiesCode0
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