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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

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Showing 3711–3720 of 9051 papers

TitleStatusHype
Human-M3: A Multi-view Multi-modal Dataset for 3D Human Pose Estimation in Outdoor ScenesCode1
FLatten Transformer: Vision Transformer using Focused Linear AttentionCode2
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness—0
The Algonauts Project 2023 Challenge: UARK-UAlbany Team SolutionCode0
BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel OptimizationCode0
Deep Image Harmonization with Learnable AugmentationCode1
Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic RegressionCode0
Identification of Driving Heterogeneity using Action-chains—0
MUSE: Multi-View Contrastive Learning for Heterophilic Graphs—0
METTS: Multilingual Emotional Text-to-Speech by Cross-speaker and Cross-lingual Emotion Transfer—0
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