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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 2401–2425 of 9051 papers

TitleStatusHype
CLearViD: Curriculum Learning for Video Description—0
Class label autoencoder for zero-shot learning—0
A Prospective-Performance Network to Alleviate Myopia in Beam Search for Response Generation—0
Designing Affine OCDM Systems with Maximum Diversity—0
A proposed new metric for the conceptual diversity of a text—0
Classifier Pool Generation based on a Two-level Diversity Approach—0
A Process for Topic Modelling Via Word Embeddings—0
Classifier-Free Guidance: From High-Dimensional Analysis to Generalized Guidance Forms—0
A Convergence indicator for Multi-Objective Optimisation Algorithms—0
A Novel Task-Oriented Text Corpus in Silent Speech Recognition and its Natural Language Generation Construction Method—0
Classifier Based Graph Construction for Video Segmentation—0
Classification Drives Geographic Bias in Street Scene Segmentation—0
A Probabilistic Bitwise Genetic Algorithm for B-Spline based Image Deformation Estimation—0
Classification and Representation via Separable Subspaces: Performance Limits and Algorithms—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
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
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
Affine Frequency Division Multiplexing With Index Modulation—0
Diversity Enhancement via Magnitude—0
Diversity for Contingency: Learning Diverse Behaviors for Efficient Adaptation and Transfer—0
Diversity-Induced Multi-View Subspace Clustering—0
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