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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 23012310 of 9051 papers

TitleStatusHype
Generative Monoculture in Large Language ModelsCode0
Bridging the Gap between Learning and Inference for Diffusion-Based Molecule GenerationCode0
Ankh: Optimized Protein Language Model Unlocks General-Purpose ModellingCode0
Bridging the Evaluation Gap: Leveraging Large Language Models for Topic Model EvaluationCode0
Bridging Information Gaps with Comprehensive Answers: Improving the Diversity and Informativeness of Follow-Up QuestionsCode0
Generative AI and Creativity: A Systematic Literature Review and Meta-AnalysisCode0
An Investigation of the (In)effectiveness of Counterfactually Augmented DataCode0
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language ModelingCode0
Topology-Preserved Human Reconstruction with DetailsCode0
Generating Realistic Forehead-Creases for User Verification via Conditioned Piecewise Polynomial CurvesCode0
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