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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 4501–4525 of 9051 papers

TitleStatusHype
Classifier-Free Guidance: From High-Dimensional Analysis to Generalized Guidance Forms—0
A Process for Topic Modelling Via Word Embeddings—0
Designing Affine OCDM Systems with Maximum Diversity—0
Emerging Diversity in a Population of Evolving Intransitive Dice—0
Emergent properties with repeated examples—0
Classifier Based Graph Construction for Video Segmentation—0
Emergent memory in cell signaling: Persistent adaptive dynamics in cascades can arise from the diversity of relaxation time-scales—0
Emergent cooperative behavior in transient compartments—0
Classification Drives Geographic Bias in Street Scene Segmentation—0
A Probabilistic Bitwise Genetic Algorithm for B-Spline based Image Deformation Estimation—0
Emergent competition shapes the ecological properties of multi-trophic ecosystems—0
Interactions and migration rescuing ecological diversity—0
Classification and Representation via Separable Subspaces: Performance Limits and Algorithms—0
Localization, epidemic transitions, and unpredictability of multistrain epidemics with an underlying genotype network—0
Class Balancing Diversity Multimodal Ensemble for Alzheimer's Disease Diagnosis and Early Detection—0
A Primer on Techtile: An R&D Testbed for Distributed Communication, Sensing and Positioning—0
Affine Frequency Division Multiplexing with Index Modulation: Full Diversity Condition, Performance Analysis, and Low-Complexity Detection—0
Embracing Diversity: Interpretable Zero-shot classification beyond one vector per class—0
Embedding Large Language Models into Extended Reality: Opportunities and Challenges for Inclusion, Engagement, and Privacy—0
Embedding-Driven Diversity Sampling to Improve Few-Shot Synthetic Data Generation—0
Class Balanced Dynamic Acquisition for Domain Adaptive Semantic Segmentation using Active Learning—0
A Preference-oriented Diversity Model Based on Mutual-information in Re-ranking for E-commerce Search—0
Embedding Cultural Diversity in Prototype-based Recommender Systems—0
Class-Aware Universum Inspired Re-Balance Learning for Long-Tailed Recognition—0
A preference elicitation interface for collecting dense recommender datasets with rich user information—0
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