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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 8576–8600 of 9051 papers

TitleStatusHype
Cultivating DNN Diversity for Large Scale Video Labelling—0
Evaluating race and sex diversity in the world's largest companies using deep neural networks—0
Learning Representations and Generative Models for 3D Point CloudsCode0
Learning Efficient Image Representation for Person Re-Identification—0
ACO for Continuous Function Optimization: A Performance Analysis—0
Learning to Avoid Errors in GANs by Manipulating Input SpacesCode0
BranchOut: Regularization for Online Ensemble Tracking With Convolutional Neural Networks—0
Diverse Image Annotation—0
Incorporating Dialectal Variability for Socially Equitable Language Identification—0
On Fairness, Diversity and Randomness in Algorithmic Decision Making—0
Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language GenerationCode0
A preference elicitation interface for collecting dense recommender datasets with rich user information—0
Image Processing in Floriculture Using a robotic Mobile Platform—0
Ranking with social cues: Integrating online review scores and popularity information—0
Long-term sustained malaria control leads to inbreeding and fragmentation of Plasmodium vivax populations—0
Gradient Diversity: a Key Ingredient for Scalable Distributed Learning—0
Diversity-aware Multi-Video Summarization—0
DeLiGAN : Generative Adversarial Networks for Diverse and Limited DataCode0
Open Loop Hyperparameter Optimization and Determinantal Point Processes—0
Batched Large-scale Bayesian Optimization in High-dimensional SpacesCode0
Towards Learned Clauses Database Reduction Strategies Based on Dominance Relationship—0
Zonotope hit-and-run for efficient sampling from projection DPPs—0
Ensemble of Part Detectors for Simultaneous Classification and Localization—0
The placement of the head that maximizes predictability. An information theoretic approach—0
Structural Compression of Convolutional Neural Networks—0
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