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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 26112620 of 9051 papers

TitleStatusHype
Analyzing the Habitable Zones of Circumbinary Planets Using Machine LearningCode0
Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and FlatnessCode0
Flickr-PAD: New Face High-Resolution Presentation Attack Detection DatabaseCode0
Determinantal Point Process as an alternative to NMSCode0
First the worst: Finding better gender translations during beam searchCode0
Analyzing the Dialect Diversity in Multi-document SummariesCode0
First U-Net Layers Contain More Domain Specific Information Than The Last OnesCode0
Diversification in Session-based News Recommender SystemsCode0
Bench4Merge: A Comprehensive Benchmark for Merging in Realistic Dense Traffic with Micro-Interactive VehiclesCode0
Detecting Visual Relationships with Deep Relational NetworksCode0
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