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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 1176–1200 of 9051 papers

TitleStatusHype
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activityCode1
Active Learning by Acquiring Contrastive ExamplesCode1
Pushing Paraphrase Away from Original Sentence: A Multi-Round Paraphrase Generation ApproachCode1
Text AutoAugment: Learning Compositional Augmentation Policy for Text ClassificationCode1
Apples to Apples: A Systematic Evaluation of Topic ModelsCode1
Plan-then-Generate: Controlled Data-to-Text Generation via PlanningCode1
AP-10K: A Benchmark for Animal Pose Estimation in the WildCode1
Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and ClassificationCode1
Subgoal Search For Complex Reasoning TasksCode1
DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based OptimizationCode1
Influence Selection for Active LearningCode1
Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code ContributionsCode1
Generating Smooth Pose Sequences for Diverse Human Motion PredictionCode1
Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd CountingCode1
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised SegmentationCode1
Diversity-based Trajectory and Goal Selection with Hindsight Experience ReplayCode1
DGCN: Diversified Recommendation with Graph Convolutional NetworksCode1
An Extensible Benchmark Suite for Learning to Simulate Physical SystemsCode1
Sketch Your Own GANCode1
IDM: An Intermediate Domain Module for Domain Adaptive Person Re-IDCode1
The Classical Language Toolkit: An NLP Framework for Pre-Modern LanguagesCode1
ProtAugment: Intent Detection Meta-Learning through Unsupervised Diverse ParaphrasingCode1
Out-of-Core Surface Reconstruction via Global TGV MinimizationCode1
Personalized Trajectory Prediction via Distribution DiscriminationCode1
Reenvisioning Collaborative Filtering vs Matrix FactorizationCode1
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