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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 1951–1975 of 9051 papers

TitleStatusHype
CommentWatcher: An Open Source Web-based platform for analyzing discussions on web forumsCode0
Improved Image Segmentation via Cost Minimization of Multiple HypothesesCode0
Improved Generalization of Weight Space Networks via AugmentationsCode0
Cross-Linguistic Syntactic Difference in Multilingual BERT: How Good is It and How Does It Affect Transfer?Code0
Aligning Sentence Simplification with ESL Learner's Proficiency for Language AcquisitionCode0
Cross-Part Learning for Fine-Grained Image ClassificationCode0
MIM-GAN-based Anomaly Detection for Multivariate Time Series DataCode0
Improved Benthic Classification using Resolution Scaling and SymmNet Unsupervised Domain AdaptationCode0
Improved Generation of Synthetic Imaging Data Using Feature-Aligned DiffusionCode0
Importance of Search and Evaluation Strategies in Neural Dialogue ModelingCode0
Implicit neural representations for joint decomposition and registration of gene expression images in the marmoset brainCode0
Implementing Smart Contracts: The case of NFT-rental with pay-per-likeCode0
Importance Weighted Expectation-Maximization for Protein Sequence DesignCode0
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output CodesCode0
Image Harmonization Dataset iHarmony4: HCOCO, HAdobe5k, HFlickr, and Hday2nightCode0
Augmented Shortcuts for Vision TransformersCode0
Combining Predictions under Uncertainty: The Case of Random Decision TreesCode0
Im2Pencil: Controllable Pencil Illustration from PhotographsCode0
Illumination Spectrum Estimation for Multispectral Images via Surface Reflectance Modeling and Spatial-Spectral Feature GenerationCode0
MirrorGAN: Learning Text-to-image Generation by RedescriptionCode0
Image Captioning via Dynamic Path CustomizationCode0
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active LearningCode0
Mitigating Sybils in Federated Learning PoisoningCode0
Imitation Learning for Sentence Generation with Dilated Convolutions Using Adversarial TrainingCode0
IIITT@LT-EDI-EACL2021-Hope Speech Detection: There is always Hope in TransformersCode0
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