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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 1976–2000 of 9051 papers

TitleStatusHype
Imitation Learning for Sentence Generation with Dilated Convolutions Using Adversarial TrainingCode0
Implementing Smart Contracts: The case of NFT-rental with pay-per-likeCode0
Importance Weighted Expectation-Maximization for Protein Sequence DesignCode0
Combining Predictions under Uncertainty: The Case of Random Decision TreesCode0
Image Harmonization Dataset iHarmony4: HCOCO, HAdobe5k, HFlickr, and Hday2nightCode0
Improved Benthic Classification using Resolution Scaling and SymmNet Unsupervised Domain AdaptationCode0
Model-Free Renewable Scenario Generation Using Generative Adversarial NetworksCode0
Improving Computed Tomography (CT) Reconstruction via 3D Shape InductionCode0
Im2Pencil: Controllable Pencil Illustration from PhotographsCode0
Illumination Spectrum Estimation for Multispectral Images via Surface Reflectance Modeling and Spatial-Spectral Feature GenerationCode0
Image Captioning via Dynamic Path CustomizationCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
Towards Understanding the Link Between Modularity and Performance in Neural Networks for Reinforcement LearningCode0
Illuminating the Diversity-Fitness Trade-Off in Black-Box OptimizationCode0
Adaptation of olfactory receptor abundances for efficient codingCode0
Illuminating the Space of Beatable Lode Runner Levels Produced By Various Generative Adversarial NetworksCode0
IIITT@DravidianLangTech-EACL2021: Transfer Learning for Offensive Language Detection in Dravidian LanguagesCode0
IIITT@LT-EDI-EACL2021-Hope Speech Detection: There is always Hope in TransformersCode0
Cultivating Archipelago of Forests: Evolving Robust Decision Trees through Island CoevolutionCode0
IIITK@LT-EDI-EACL2021: Hope Speech Detection for Equality, Diversity, and Inclusion in Tamil , Malayalam and EnglishCode0
Colorful Image ColorizationCode0
Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural FeaturesCode0
A Unified Substrate for Body-Brain Co-evolutionCode0
A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN FrameworkCode0
IDIAP Submission@LT-EDI-ACL2022: Homophobia/Transphobia Detection in social media commentsCode0
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