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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

Showing 44114420 of 9051 papers

TitleStatusHype
Active learning for medical image segmentation with stochastic batchesCode1
Robust Scheduling with GFlowNets0
PECAN: Leveraging Policy Ensemble for Context-Aware Zero-Shot Human-AI CoordinationCode0
Evaluating clinical diversity and plausibility of synthetic capsule endoscopic images0
Antenna Array Calibration Via Gaussian Process Models0
Ankh: Optimized Protein Language Model Unlocks General-Purpose ModellingCode0
BuildSeg: A General Framework for the Segmentation of Buildings0
Deep Diversity-Enhanced Feature Representation of Hyperspectral ImagesCode1
Music Playlist Title Generation Using Artist InformationCode0
A Comprehensive Review of Data-Driven Co-Speech Gesture Generation0
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