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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 8851–8900 of 9051 papers

TitleStatusHype
Spatial measures of genetic heterogeneity during carcinogenesis—0
Feature-Based Diversity Optimization for Problem Instance Classification—0
Evolutionary Landscape and Management of Population Diversity—0
The Complexity of Genetic Diversity—0
New thoughts on an old riddle: what determines genetic diversity within and between species?—0
Unsupervised Ensemble Learning with Dependent Classifiers—0
Entropy, Ergodicity and Stem Cell Multipotency—0
Quantification of the effect of mutations using a global probability model of natural sequence variation—0
Diverse Yet Efficient Retrieval using Hash Functions—0
Beyond Spatial Pyramid Matching: Space-time Extended Descriptor for Action Recognition—0
Corpus COFLA: A research corpus for the Computational study of Flamenco Music—0
A Diversity-Promoting Objective Function for Neural Conversation ModelsCode0
Diverse Large-Scale ITS Dataset Created from Continuous Learning for Real-Time Vehicle Detection—0
Understand Scene Categories by Objects: A Semantic Regularized Scene Classifier Using Convolutional Neural Networks—0
An Approach to the Analysis of the South Slavic Medieval Labels Using Image Texture—0
Efficient Sampling for k-Determinantal Point Processes—0
Detecting patterns of species diversification in the presence of both rate shifts and mass extinctions—0
Linked Component Analysis from Matrices to High Order Tensors: Applications to Biomedical Data—0
ESDF: Ensemble Selection using Diversity and Frequency—0
Generation of Multimedia Artifacts: An Extractive Summarization-based Approach—0
Crowd Access Path Optimization: Diversity Matters—0
Multi-Label Active Learning from Crowds—0
Evolutionary Multimodal Optimization: A Short Survey—0
On Hyperspectral Classification in the Compressed Domain—0
Dictionary and Image Recovery from Incomplete and Random Measurements—0
Inverse Relationship Between Molecular Diversity And Resource Abundances—0
Diffusion Adaptation Over Clustered Multitask Networks Based on the Affine Projection Algorithm—0
The geometry of relative arbitrage—0
Requirements for Open-Ended Evolution in Natural and Artificial Systems—0
STICK: Spike Time Interval Computational Kernel, A Framework for General Purpose Computation using Neurons, Precise Timing, Delays, and SynchronyCode0
Bandit-Based Task Assignment for Heterogeneous Crowdsourcing—0
Diversity improves performance in excitable networks—0
An Efficient Genetic Algorithm for Discovering Diverse-Frequent Patterns—0
Diversity waves in collapse-driven population dynamics—0
Well-temperate phage: optimal bet-hedging against local environmental collapses—0
Model of human collective decision-making in complex environments—0
Parsimonious Labeling—0
Evaluation of Genotypic Diversity Measurements Exploited in Real-Coded Representation—0
Top-N recommendations in the presence of sparsity: An NCD-based approach—0
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading BooksCode1
A guide through a family of phylogenetic dissimilarity measures among sites—0
Genetic Algorithms for multimodal optimization: a review—0
Measuring Feature Diversity in Native Language Identification—0
Watch and Learn: Semi-Supervised Learning for Object Detectors From Video—0
Material Classification With Thermal Imagery—0
Diversity-Induced Multi-View Subspace Clustering—0
Classifier Based Graph Construction for Video Segmentation—0
Diversity in Spectral Learning for Natural Language Parsing—0
Improved Deep Convolutional Neural Network For Online Handwritten Chinese Character Recognition using Domain-Specific Knowledge—0
Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos—0
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