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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 26212630 of 9051 papers

TitleStatusHype
Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and FlatnessCode0
Flexible Modeling of Diversity with Strongly Log-Concave DistributionsCode0
Bench4Merge: A Comprehensive Benchmark for Merging in Realistic Dense Traffic with Micro-Interactive VehiclesCode0
Detecting Visual Relationships with Deep Relational NetworksCode0
First the worst: Finding better gender translations during beam searchCode0
Finer Metagenomic Reconstruction via Biodiversity OptimizationCode0
FireFly A Synthetic Dataset for Ember Detection in WildfireCode0
First U-Net Layers Contain More Domain Specific Information Than The Last OnesCode0
3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single ImageCode0
Fine-Grained Spatiotemporal Motion Alignment for Contrastive Video Representation LearningCode0
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