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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 801–810 of 9051 papers

TitleStatusHype
TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer CapabilitiesCode1
Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIVCode1
Generative Category-Level Shape and Pose Estimation with Semantic PrimitivesCode1
Generative Data Augmentation for Aspect Sentiment Quad PredictionCode1
Generative Fractional Diffusion ModelsCode1
Are Large Language Models Capable of Generating Human-Level Narratives?Code1
Addressing the Elephant in the Room: Robust Animal Re-Identification with Unsupervised Part-Based Feature AlignmentCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
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