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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 49814990 of 9051 papers

TitleStatusHype
repgenHMM: a dynamic programming tool to infer the rules of immune receptor generation from sequence data0
Replica Exchange using q-Gaussian Swarm Quantum Particle Intelligence Method0
Replicate immunosequencing as a robust probe of B cell repertoire diversity0
Representational Ethical Model Calibration0
Representation Heterogeneity0
Representation Learning Beyond Linear Prediction Functions0
Representation Online Matters: Practical End-to-End Diversification in Search and Recommender Systems0
Representing Interlingual Meaning in Lexical Databases0
Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference0
Repurformer: Transformers for Repurposing-Aware Molecule Generation0
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