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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

Showing 15011525 of 9051 papers

TitleStatusHype
APOLLO: An Optimized Training Approach for Long-form Numerical ReasoningCode1
exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformer ModelsCode1
Automatic Data Augmentation for 3D Medical Image SegmentationCode1
Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros StudyCode1
Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain NavigationCode1
Clotho: An Audio Captioning DatasetCode1
Improving Integrated Gradient-based Transferable Adversarial Examples by Refining the Integration PathCode1
Scaling-up Disentanglement for Image TranslationCode1
IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion ModelsCode1
Explain Me the Painting: Multi-Topic Knowledgeable Art Description GenerationCode1
CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language ModelsCode1
Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active ExplorationCode1
Score-Based Generative Models for Designing Binding Peptide BackbonesCode1
Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse GranularityCode1
Exploring Empty Spaces: Human-in-the-Loop Data AugmentationCode1
Co-Mixup: Saliency Guided Joint Mixup with Supermodular DiversityCode1
Exploring Inter-Channel Correlation for Diversity-preserved KnowledgeDistillationCode1
Exploring Inter-Channel Correlation for Diversity-Preserved Knowledge DistillationCode1
Exploring Semantic Consistency and Style Diversity for Domain Generalized Semantic SegmentationCode1
CIAGAN: Conditional Identity Anonymization Generative Adversarial NetworksCode1
Selective Prompting Tuning for Personalized Conversations with LLMsCode1
CIC: Contrastive Intrinsic Control for Unsupervised Skill DiscoveryCode1
A Map of Diverse Synthetic Stable Roommates InstancesCode1
Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial NetworksCode1
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual GroundingCode1
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