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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 6876–6900 of 9051 papers

TitleStatusHype
Unsupervised Fine-Tuning Data Selection for ASR Using Self-Supervised Speech Models—0
RankAug: Augmented data ranking for text classification—0
Rank diversity of languages: Generic behavior in computational linguistics—0
Ranking-aware Uncertainty for Text-guided Image Retrieval—0
Unsupervised Hierarchical Story Infilling—0
Ranking in Genealogy: Search Results Fusion at Ancestry—0
A blindspot of AI ethics: anti-fragility in statistical prediction—0
Ranking with social cues: Integrating online review scores and popularity information—0
Rapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado event—0
RAP-Net: Recurrent Attention Pooling Networks for Dialogue Response Selection—0
Unsupervised inference approach to facial attractiveness—0
Rare recombination events generate sequence diversity among balancer chromosomes in Drosophila melanogaster—0
An Improved Grey Wolf Optimization Algorithm for Heart Disease Prediction—0
RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for Real-world Applications—0
Unsupervised Learning of Compositional Scene Representations from Multiple Unspecified Viewpoints—0
RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow Trajectories—0
R-BI: Regularized Batched Inputs enhance Incremental Decoding Framework for Low-Latency Simultaneous Speech Translation—0
An Improved Dung Beetle Optimizer for Random Forest Optimization—0
RD-DPP: Rate-Distortion Theory Meets Determinantal Point Process to Diversify Learning Data Samples—0
RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation—0
Unsupervised Learning of Depth Estimation and Visual Odometry for Sparse Light Field Cameras—0
REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning—0
Reading Recognition in the Wild—0
Reading Yule in light of the history and present of macroevolution—0
Wide Aspect Ratio Matching for Robust Face Detection—0
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