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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 3111–3120 of 9051 papers

TitleStatusHype
Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training—0
EnDive: A Cross-Dialect Benchmark for Fairness and Performance in Large Language Models—0
End-to-End 3D Multi-Object Tracking and Trajectory Forecasting—0
CLIP-Gaze: Towards General Gaze Estimation via Visual-Linguistic Model—0
End-to-End Adversarial Learning for Intrusion Detection in Computer Networks—0
End-to-End Autoregressive Retrieval via Bootstrapping for Smart Reply Systems—0
An Investigation of the (In)effectiveness of Counterfactually Augmented Data—0
End-to-end Learnable Diversity-aware News Recommendation—0
A Combined CNN and LSTM Model for Arabic Sentiment Analysis—0
Diversity and Diffusion: Observations on Synthetic Image Distributions with Stable Diffusion—0
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