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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 2711–2720 of 9051 papers

TitleStatusHype
Effective Demonstration Annotation for In-Context Learning via Language Model-Based Determinantal Point Process—0
Effects of discordance between species and gene trees on phylogenetic diversity conservation—0
CADMR: Cross-Attention and Disentangled Learning for Multimodal Recommender Systems—0
CAARMA: Class Augmentation with Adversarial Mixup Regularization—0
Anomaly Detection in Video Sequence With Appearance-Motion Correspondence—0
Advancing Video Anomaly Detection: A Concise Review and a New Dataset—0
C2AM Loss: Chasing a Better Decision Boundary for Long-Tail Object Detection—0
Advances in Robust Federated Learning: Heterogeneity Considerations—0
Burn After Reading: Online Adaptation for Cross-domain Streaming Data—0
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning—0
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