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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 4691–4700 of 9051 papers

TitleStatusHype
Bootstrapping NLP tools across low-resourced African languages: an overview and prospects—0
Deep Reinforcement Learning for Inverse Inorganic Materials Design—0
Generative Range Imaging for Learning Scene Priors of 3D LiDAR DataCode1
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation—0
JRDB-Pose: A Large-scale Dataset for Multi-Person Pose Estimation and Tracking—0
Similarity of Neural Architectures using Adversarial Attack Transferability—0
Local intraspecific aggregation in phytoplankton model communities: spatial scales of occurrence and implications for coexistenceCode0
Discovering Many Diverse Solutions with Bayesian OptimizationCode0
Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified RecommendationCode0
Spatio-channel Attention Blocks for Cross-modal Crowd CountingCode1
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