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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 2111–2120 of 9051 papers

TitleStatusHype
Data Ethics in the Era of Healthcare Artificial Intelligence in Africa: An Ubuntu Philosophy Perspective—0
LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal DataCode1
Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks—0
T-JEPA: A Joint-Embedding Predictive Architecture for Trajectory Similarity Computation—0
Contextual Distillation Model for Diversified Recommendation—0
Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning—0
Decoding the Diversity: A Review of the Indic AI Research Landscape—0
Meta-Learning an Evolvable Developmental EncodingCode0
Optimal Kernel Orchestration for Tensor Programs with KorchCode1
StableMaterials: Enhancing Diversity in Material Generation via Semi-Supervised Learning—0
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