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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 1876–1900 of 9051 papers

TitleStatusHype
A Tool for Super-Resolving Multimodal Clinical MRICode0
Complete 3D Scene Parsing from an RGBD ImageCode0
Improving Ensemble Distillation With Weight Averaging and Diversifying PerturbationCode0
Improving End-to-End Sequential Recommendations with Intent-aware DiversificationCode0
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial RobustnessCode0
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQLCode0
Improving Diversity of Commonsense Generation by Large Language Models via In-Context LearningCode0
Improving Generalization with Domain Convex GameCode0
LiMiT: The Literal Motion in Text DatasetCode0
Competition and Diversity in Generative AICode0
Albumentations: fast and flexible image augmentationsCode0
Improving Adversarial Robustness via Decoupled Visual Representation MaskingCode0
Comparison of Diverse Decoding Methods from Conditional Language ModelsCode0
Improving Computed Tomography (CT) Reconstruction via 3D Shape InductionCode0
LLM-TOPLA: Efficient LLM Ensemble by Maximising DiversityCode0
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output CodesCode0
Artificial Immune System of Secure Face Recognition Against Adversarial AttacksCode0
Improved Image Segmentation via Cost Minimization of Multiple HypothesesCode0
Local intraspecific aggregation in phytoplankton model communities: spatial scales of occurrence and implications for coexistenceCode0
ABD-Net: Attentive but Diverse Person Re-IdentificationCode0
Improving Contextualized Topic Models with Negative SamplingCode0
Importance of Search and Evaluation Strategies in Neural Dialogue ModelingCode0
Attesting Distributional Properties of Training Data for Machine LearningCode0
Correlation and Navigation in the Vocabulary Key Representation Space of Language ModelsCode0
Importance Weighted Expectation-Maximization for Protein Sequence DesignCode0
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