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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 971980 of 9051 papers

TitleStatusHype
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Modality Plug-and-Play: Elastic Modality Adaptation in Multimodal LLMs for Embodied AICode1
Aligning Language Models with Preferences through f-divergence MinimizationCode1
Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word ExclusionCode1
AugMax: Adversarial Composition of Random Augmentations for Robust TrainingCode1
Aligning Latent and Image Spaces to Connect the UnconnectableCode1
Monocular Human-Object Reconstruction in the WildCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Continual Object Detection via Prototypical Task Correlation Guided Gating MechanismCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
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