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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 1611–1620 of 9051 papers

TitleStatusHype
Bias Begets Bias: The Impact of Biased Embeddings on Diffusion Models—0
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language ModelingCode0
Enhancing Data Quality through Self-learning on Imbalanced Financial Risk Data—0
A Compressive Memory-based Retrieval Approach for Event Argument Extraction—0
Towards Diverse and Efficient Audio Captioning via Diffusion Models—0
LawDNet: Enhanced Audio-Driven Lip Synthesis via Local Affine Warping DeformationCode0
Towards Precision Characterization of Communication Disorders using Models of Perceived Pragmatic Similarity—0
Frequency Tracking Features for Data-Efficient Deep Siren IdentificationCode0
Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech—0
Multi-intent Aware Contrastive Learning for Sequential Recommendation—0
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