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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 811820 of 9051 papers

TitleStatusHype
Addressing the Elephant in the Room: Robust Animal Re-Identification with Unsupervised Part-Based Feature AlignmentCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
Towards Geospatial Foundation Models via Continual PretrainingCode1
Action detection using a neural network elucidates the genetics of mouse grooming behaviorCode1
GMOCAT: A Graph-Enhanced Multi-Objective Method for Computerized Adaptive TestingCode1
A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open ChallengesCode1
A Bayesian Flow Network Framework for Chemistry TasksCode1
Continual Object Detection via Prototypical Task Correlation Guided Gating MechanismCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
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