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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 9001–9025 of 9051 papers

TitleStatusHype
Diversity and Social Network Structure in Collective Decision Making: Evolutionary Perspectives with Agent-Based Simulations—0
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints—0
Multilabel Classification through Random Graph Ensembles—0
Mapping the stereotyped behaviour of freely-moving fruit fliesCode0
A Computational Model of Two Cognitive Transitions Underlying Cultural Evolution—0
An Agent-based Model of the Cognitive Mechanisms Underlying the Origins of Creative Cultural Evolution—0
A Systematic Exploration of Diversity in Machine Translation—0
EVOC: A Computer Model of the Evolution of Culture—0
Meme and Variations: A Computer Model of Cultural Evolution—0
A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics—0
Why SOV might be initially preferred and then lost or recovered? A theoretical framework—0
Language diversity and implications for Language technology in the Multilingual Europe—0
Sparse and Non-Negative BSS for Noisy DataCode0
How Did Humans Become So Creative? A Computational Approach—0
Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization—0
Positive Diversity Tuning for Machine Translation System Combination—0
An Overview of the Research on Texture Based Plant Leaf Classification—0
DAEBAK!: Peripheral Diversity for Multilingual Word Sense Disambiguation—0
Continuous Inference in Graphical Models with Polynomial Energies—0
Part Discovery from Partial Correspondence—0
Tracking Sports Players with Context-Conditioned Motion Models—0
Graph-Based Discriminative Learning for Location Recognition—0
Story-Driven Summarization for Egocentric Video—0
Improving NSGA-II with an Adaptive Mutation Operator—0
Learning Policies for Contextual Submodular Prediction—0
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