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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 5371–5380 of 9051 papers

TitleStatusHype
SP-BatikGAN: An Efficient Generative Adversarial Network for Symmetric Pattern Generation—0
Speaker diarisation using 2D self-attentive combination of embeddings—0
Speciation in a MacArthur model predicts growth, stability and adaptation in ecosystems dynamics—0
Sparse species interactions reproduce abundance correlation patterns in microbial communities—0
Spectral and Polarization Vision: Spectro-polarimetric Real-world Dataset—0
Spectral Policy Optimization: Coloring your Incorrect Reasoning in GRPO—0
SPECTRUM: Speaker-Enhanced Pre-Training for Long Dialogue Summarization—0
Speech Corpora Divergence Based Unsupervised Data Selection for ASR—0
Speech Recognition with Augmented Synthesized Speech—0
Speed Co-Augmentation for Unsupervised Audio-Visual Pre-training—0
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