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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 30813090 of 9051 papers

TitleStatusHype
Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational CostsCode0
Regularizing Variational Autoencoder with Diversity and Uncertainty AwarenessCode0
Automatic Fused Multimodal Deep Learning for Plant IdentificationCode0
Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering SystemsCode0
Ensembles of Locally Independent Prediction ModelsCode0
EnsLM: Ensemble Language Model for Data Diversity by Semantic ClusteringCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating AttentionCode0
Ensemble Distribution DistillationCode0
Enhancing Visual Dialog Questioner with Entity-based Strategy Learning and Augmented GuesserCode0
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