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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 42314240 of 9051 papers

TitleStatusHype
Distribution-restrained Softmax Loss for the Model Robustness0
Experimental Validation of Single BS 5G mmWave Positioning and Mapping for Intelligent Transport0
Active Coarse-to-Fine Segmentation of Moveable Parts from Real Images0
Towards Reliable Neural Machine Translation with Consistency-Aware Meta-Learning0
EPiC: Ensemble of Partial Point Clouds for Robust ClassificationCode0
Improved Benthic Classification using Resolution Scaling and SymmNet Unsupervised Domain AdaptationCode0
Pluralistic Aging Diffusion Autoencoder0
Two Kinds of Recall0
Diffusion-based Target Sampler for Unsupervised Domain Adaptation0
Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out0
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