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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 8826–8850 of 9051 papers

TitleStatusHype
Sampling from Probabilistic Submodular Models—0
Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization—0
SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals—0
Fast and High Quality Highlight Removal from A Single Image—0
An analytically tractable model for community ecology with many species—0
Protein residue networks from a local search perspective—0
Incidental Scene Text Understanding: Recent Progresses on ICDAR 2015 Robust Reading Competition Challenge 4—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
Exponential Natural Particle Filter—0
Semantic Diversity versus Visual Diversity in Visual Dictionaries—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
Rare recombination events generate sequence diversity among balancer chromosomes in Drosophila melanogaster—0
Diversity Networks: Neural Network Compression Using Determinantal Point ProcessesCode0
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
Semantic Summarization of Egocentric Photo Stream EventsCode0
repgenHMM: a dynamic programming tool to infer the rules of immune receptor generation from sequence data—0
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