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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 28312840 of 9051 papers

TitleStatusHype
Generating Diverse Translation with Perturbed kNN-MT0
UMOEA/D: A Multiobjective Evolutionary Algorithm for Uniform Pareto Objectives based on Decomposition0
Dataset Clustering for Improved Offline Policy LearningCode0
DUEL: Duplicate Elimination on Active Memory for Self-Supervised Class-Imbalanced Learning0
MaxMin-RLHF: Alignment with Diverse Human Preferences0
MultiMedEval: A Benchmark and a Toolkit for Evaluating Medical Vision-Language ModelsCode2
One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill0
Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model0
Topic Modeling as Multi-Objective Contrastive Optimization0
Understanding fitness landscapes in morpho-evolution via local optima networks0
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