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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 29212930 of 9051 papers

TitleStatusHype
Exploratory State Representation LearningCode0
A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial DiversityCode0
Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous DrivingCode0
Exploring Model Consensus to Generate Translation ParaphrasesCode0
Expanding functional protein sequence space using generative adversarial networksCode0
Deep Active Learning: Unified and Principled Method for Query and TrainingCode0
Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible TemplatesCode0
exHarmony: Authorship and Citations for Benchmarking the Reviewer Assignment ProblemCode0
Exhaustive Exploitation of Nature-inspired Computation for Cancer Screening in an Ensemble MannerCode0
Exact Fusion via Feature Distribution Matching for Few-shot Image GenerationCode0
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