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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 8801–8850 of 9051 papers

TitleStatusHype
R\'enyi entropy, abundance distribution and the equivalence of ensembles—0
Diversity in Object Proposals—0
Site-specific recombinatorics: in situ cellular barcoding with the Cre Lox system—0
Progress towards quantitative design principles of multicellular systems—0
A multilayer PPI network analysis of different life stages in C. elegans—0
Low-Rank Factorization of Determinantal Point Processes for RecommendationCode0
Bayesian identification of bacterial strains from sequencing dataCode0
A tool for computing diversity and consideration on differences between diversity indices—0
Generating Discriminative Object Proposals via Submodular Ranking—0
Homogeneity of Cluster Ensembles—0
Daleel: Simplifying Cloud Instance Selection Using Machine Learning—0
An ensemble diversity approach to supervised binary hashing—0
Font Identification in Historical Documents Using Active Learning—0
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural ImagesCode0
Improved Spoken Document Summarization with Coverage Modeling Techniques—0
A Simple Measure of Economic Complexity—0
Toward a Robust Diversity-Based Model to Detect Changes of Context—0
Mutual Information and Diverse Decoding Improve Neural Machine TranslationCode0
Optimal Selective Attention in Reactive Agents—0
Diversity Enhancement for Micro-Differential Evolution—0
Latent Variable Modeling with Diversity-Inducing Mutual Angular Regularization—0
Feature Selection for Classification under Anonymity Constraint—0
Shannon Mutual Information Applied to Genetic Systems—0
MovieQA: Understanding Stories in Movies through Question-AnsweringCode0
SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals—0
Fast and High Quality Highlight Removal from A Single Image—0
Sampling from Probabilistic Submodular Models—0
Inferring M-Best Diverse Labelings in a Single One—0
Understanding Everyday Hands in Action From RGB-D Images—0
Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization—0
An Empirical Study of Diversity of Word Alignment and its Symmetrization Techniques for System Combination—0
Incidental Scene Text Understanding: Recent Progresses on ICDAR 2015 Robust Reading Competition Challenge 4—0
An analytically tractable model for community ecology with many species—0
Protein residue networks from a local search perspective—0
Diversity of immune strategies explained by adaptation to pathogen statistics—0
Commet: comparing and combining multiple metagenomic datasetsCode0
Adaptability of non-genetic diversity in bacterial chemotaxis—0
On the Generalization Error Bounds of Neural Networks under Diversity-Inducing Mutual Angular Regularization—0
Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization—0
Semantic Diversity versus Visual Diversity in Visual Dictionaries—0
Exponential Natural Particle Filter—0
Data Representation and Compression Using Linear-Programming Approximations—0
Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks—0
Cnidaria: fast, reference-free clustering of raw and assembled genome and transcriptome NGS data—0
Diversity Networks: Neural Network Compression Using Determinantal Point ProcessesCode0
Rare recombination events generate sequence diversity among balancer chromosomes in Drosophila melanogaster—0
Requirements for efficient cell-type proportioning: regulatory timescales, stochasticity and lateral inhibition—0
Max-Sum Diversification, Monotone Submodular Functions and Semi-metric Spaces—0
Population size predicts lexical diversity, but so does the mean sea level - why it is important to correctly account for the structure of temporal data—0
Data-Driven Learning of a Union of Sparsifying Transforms Model for Blind Compressed Sensing—0
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