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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 7826–7850 of 9051 papers

TitleStatusHype
5G framework concepts for the next generation networks—0
Submodular Batch Selection for Training Deep Neural NetworksCode0
Bayesian Optimisation over Multiple Continuous and Categorical InputsCode0
Training on test data: Removing near duplicates in Fashion-MNIST—0
GAIT: A Geometric Approach to Information TheoryCode0
Batch Active Learning Using Determinantal Point ProcessesCode0
SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data FusionCode1
Modeling Semantic Relationship in Multi-turn Conversations with Hierarchical Latent Variables—0
Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles—0
A concise guide to existing and emerging vehicle routing problem variants—0
Comparison of Diverse Decoding Methods from Conditional Language ModelsCode0
Neural Response Generation with Meta-Words—0
A K-means-based Multi-subpopulation Particle Swarm Optimization for Neural Network Ensemble—0
Flexible Modeling of Diversity with Strongly Log-Concave DistributionsCode0
Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites—0
Federated Learning for Emoji Prediction in a Mobile Keyboard—0
Simultaneously Learning Architectures and Features of Deep Neural Networks—0
Suppressing Model Overfitting for Image Super-Resolution Networks—0
Landslide Geohazard Assessment With Convolutional Neural Networks Using Sentinel-2 Imagery Data—0
Patch Transformer for Multi-tagging Whole Slide Histopathology Images—0
Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse DatasetCode1
Improving Neural Language Modeling via Adversarial TrainingCode0
Exploration and Exploitation in Symbolic Regression using Quality-Diversity and Evolutionary Strategies Algorithms—0
Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task—0
Argument Generation with Retrieval, Planning, and Realization—0
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