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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 10511060 of 9051 papers

TitleStatusHype
EmpHi: Generating Empathetic Responses with Human-like IntentsCode1
Determinantal Point Process Likelihoods for Sequential RecommendationCode1
Efficient Neural Neighborhood Search for Pickup and Delivery ProblemsCode1
EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text ClassificationCode1
Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of ExplanationsCode1
MAP-Elites based Hyper-Heuristic for the Resource Constrained Project Scheduling ProblemCode1
Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled DataCode1
Attributed Graph Clustering with Dual Redundancy ReductionCode1
Language-Grounded Indoor 3D Semantic Segmentation in the WildCode1
Procedural Content Generation using Neuroevolution and Novelty Search for Diverse Video Game LevelsCode1
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