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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 8551–8575 of 9051 papers

TitleStatusHype
Algorithmically probable mutations reproduce aspects of evolution such as convergence rate, genetic memory, and modularity—0
ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?Code0
Towards Automatic Construction of Diverse, High-quality Image Dataset—0
DesnowNet: Context-Aware Deep Network for Snow Removal—0
Image Augmentation using Radial Transform for Training Deep Neural Networks—0
Divide and Fuse: A Re-ranking Approach for Person Re-identification—0
Document Image Binarization with Fully Convolutional Neural Networks—0
STARDATA: A StarCraft AI Research DatasetCode0
GPLAC: Generalizing Vision-Based Robotic Skills using Weakly Labeled Images—0
Optimizing Region Selection for Weakly Supervised Object Detection—0
Uncorrelation and Evenness: a New Diversity-Promoting Regularizer—0
Learning Latent Space Models with Angular Constraints—0
Predicting Success in Goal-Driven Human-Human Dialogues—0
Modeling Context Words as Regions: An Ordinal Regression Approach to Word Embedding—0
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasmCode0
Model-Free Renewable Scenario Generation Using Generative Adversarial NetworksCode0
Exploiting Web Images for Weakly Supervised Object Detection—0
Feature Extraction via Recurrent Random Deep Ensembles and its Application in Gruop-level Happiness Estimation—0
Autocompletion interfaces make crowd workers slower, but their use promotes response diversity—0
DeepPath: A Reinforcement Learning Method for Knowledge Graph ReasoningCode0
Crowdsourcing Multiple Choice Science Questions—0
Learning to select data for transfer learning with Bayesian OptimizationCode0
Coexistence of critical sensitivity and subcritical specificity can yield optimal population coding—0
Capturing the diversity of biological tuning curves using generative adversarial networks—0
Learning Photography Aesthetics with Deep CNNsCode0
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