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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 3911–3920 of 9051 papers

TitleStatusHype
Data Quality in Imitation Learning—0
DOS: Diverse Outlier Sampling for Out-of-Distribution DetectionCode0
GPT-FL: Generative Pre-trained Model-Assisted Federated LearningCode1
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation—0
Knowledge of cultural moral norms in large language modelsCode0
KL-Divergence Guided Temperature SamplingCode0
On Feature Diversity in Energy-based Models—0
DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation—0
Some voices are too common: Building fair speech recognition systems using the Common Voice dataset—0
LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity OptimizationCode1
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