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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 3831–3840 of 9051 papers

TitleStatusHype
Diverse Neural Network Learns True Target Functions—0
From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning—0
Boosting Dialog Response Generation—0
From Shadow Segmentation to Shadow Removal—0
DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery—0
Covariance Matrix Adaptation MAP-Annealing—0
DiverseMotion: Towards Diverse Human Motion Generation via Discrete Diffusion—0
From text to talk: Harnessing conversational corpora for humane and diversity-aware language technology—0
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control—0
Diverse mini-batch Active Learning—0
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