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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 876–900 of 9051 papers

TitleStatusHype
Neural Video Compression with Diverse ContextsCode1
Key-Exchange Convolutional Auto-Encoder for Data Augmentation in Early Knee Osteoarthritis DetectionCode1
Tailoring Language Generation Models under Total Variation DistanceCode1
Diverse Policy Optimization for Structured Action SpaceCode1
AfriSenti: A Twitter Sentiment Analysis Benchmark for African LanguagesCode1
Aligning Language Models with Preferences through f-divergence MinimizationCode1
NL2CMD: An Updated Workflow for Natural Language to Bash Commands TranslationCode1
Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial ExamplesCode1
Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using SamplesCode1
Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal AnchorsCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
Towards Geospatial Foundation Models via Continual PretrainingCode1
Mask Conditional Synthetic Satellite ImageryCode1
MMPD: Multi-Domain Mobile Video Physiology DatasetCode1
Sample-efficient Multi-objective Molecular Optimization with GFlowNetsCode1
Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable DiffusionCode1
LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form ControlCode1
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving PlannersCode1
ANTM: An Aligned Neural Topic Model for Exploring Evolving TopicsCode1
Evolving Flying Machines in Minecraft Using Quality DiversityCode1
ProtoSeg: Interpretable Semantic Segmentation with Prototypical PartsCode1
Data Augmentation Alone Can Improve Adversarial TrainingCode1
Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon PredictionCode1
Active learning for medical image segmentation with stochastic batchesCode1
Deep Diversity-Enhanced Feature Representation of Hyperspectral ImagesCode1
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