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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 1951–1960 of 9051 papers

TitleStatusHype
A blindspot of AI ethics: anti-fragility in statistical prediction—0
Confidence-Guided Semi-supervised Learning in Land Cover Classification—0
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout—0
Cross-modal Face- and Voice-style Transfer—0
Active Learning for Object Detection with Non-Redundant Informative Sampling—0
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation—0
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech—0
Condorcet's Jury Theorem for Consensus Clustering and its Implications for Diversity—0
Condition-Transforming Variational AutoEncoder for Conversation Response Generation—0
ASR4REAL: An extended benchmark for speech models—0
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