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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 29312940 of 9051 papers

TitleStatusHype
ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist ExaminationCode0
Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural NetworkCode0
Explaining crime diversity with Google street viewCode0
Expanding functional protein sequence space using generative adversarial networksCode0
Exhaustive Exploitation of Nature-inspired Computation for Cancer Screening in an Ensemble MannerCode0
Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible TemplatesCode0
exHarmony: Authorship and Citations for Benchmarking the Reviewer Assignment ProblemCode0
VideoDG: Generalizing Temporal Relations in Videos to Novel DomainsCode0
Evolvability ES: Scalable and Direct Optimization of EvolvabilityCode0
Evolutionary Generative Adversarial NetworksCode0
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