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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 771780 of 9051 papers

TitleStatusHype
DVG-Face: Dual Variational Generation for Heterogeneous Face RecognitionCode1
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
Towards Geospatial Foundation Models via Continual PretrainingCode1
Global Tensor Motion PlanningCode1
GMOCAT: A Graph-Enhanced Multi-Objective Method for Computerized Adaptive TestingCode1
A Quantum Leaky Integrate-and-Fire Spiking Neuron and NetworkCode1
G-Eval: NLG Evaluation using GPT-4 with Better Human AlignmentCode1
CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context DemonstrationsCode1
DSDL: Data Set Description Language for Bridging Modalities and Tasks in AI DataCode1
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