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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

Showing 781790 of 9051 papers

TitleStatusHype
AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand PoseCode1
ARBERT & MARBERT: Deep Bidirectional Transformers for ArabicCode1
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational ReasoningCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
Group-wise Inhibition based Feature Regularization for Robust ClassificationCode1
Dual-stage Hyperspectral Image Classification Model with Spectral SupertokenCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
Dual Feature Augmentation Network for Generalized Zero-shot LearningCode1
Addressing the Elephant in the Room: Robust Animal Re-Identification with Unsupervised Part-Based Feature AlignmentCode1
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