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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 11011110 of 9051 papers

TitleStatusHype
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill DiversityCode1
TIML: Task-Informed Meta-Learning for AgricultureCode1
Lipschitz-constrained Unsupervised Skill DiscoveryCode1
CIC: Contrastive Intrinsic Control for Unsupervised Skill DiscoveryCode1
NeuralTailor: Reconstructing Sewing Pattern Structures from 3D Point Clouds of GarmentsCode1
Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement LearningCode1
Variational Neural Cellular AutomataCode1
You Only Cut Once: Boosting Data Augmentation with a Single CutCode1
Any-Play: An Intrinsic Augmentation for Zero-Shot CoordinationCode1
Variational Model Inversion AttacksCode1
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