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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
Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language ModelsCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
IR-BERT: Leveraging BERT for Semantic Search in Background Linking for News ArticlesCode1
Active learning for medical image segmentation with stochastic batchesCode1
Controllable Open-ended Question Generation with A New Question Type OntologyCode1
Controllable Video Captioning with an Exemplar SentenceCode1
Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoECode1
Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image AnalysisCode1
Keiki: Towards Realistic Danmaku Generation via Sequential GANsCode1
KERPLE: Kernelized Relative Positional Embedding for Length ExtrapolationCode1
Key-Exchange Convolutional Auto-Encoder for Data Augmentation in Early Knee Osteoarthritis DetectionCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Knowledge Extraction and Distillation from Large-Scale Image-Text Colonoscopy Records Leveraging Large Language and Vision ModelsCode1
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language ModelsCode1
KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
Controllable Multi-Interest Framework for RecommendationCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Contrastive Syn-to-Real GeneralizationCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Large Scale Image Completion via Co-Modulated Generative Adversarial NetworksCode1
Large-scale Unsupervised Semantic SegmentationCode1
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
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