SOTAVerified

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 27712780 of 9051 papers

TitleStatusHype
DiffuCOMET: Contextual Commonsense Knowledge DiffusionCode0
TrustMol: Trustworthy Inverse Molecular Design via Alignment with Molecular Dynamics0
Taming the Tail in Class-Conditional GANs: Knowledge Sharing via Unconditional Training at Lower ResolutionsCode1
Against Filter Bubbles: Diversified Music Recommendation via Weighted Hypergraph Embedding Learning0
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts0
On Languaging a Simulation Engine0
Topic-to-essay generation with knowledge-based content selection0
Finding Near-Optimal Portfolios With Quality-Diversity0
ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion ModelingCode0
Pfeed: Generating near real-time personalized feeds using precomputed embedding similarities0
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