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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 2351–2375 of 9051 papers

TitleStatusHype
GenZSL: Generative Zero-Shot Learning Via Inductive Variational AutoencoderCode0
Generative Monoculture in Large Language ModelsCode0
BOLD5000: A public fMRI dataset of 5000 imagesCode0
A Diversity-Promoting Objective Function for Neural Conversation ModelsCode0
Genetic Algorithm with Innovative Chromosome Patterns in the Breeding ProcessCode0
Block Flow: Learning Straight Flow on Data BlocksCode0
A Diversity-Enhanced Knowledge Distillation Model for Practical Math Word Problem SolvingCode0
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language ModelingCode0
A diversity-enhanced genetic algorithm for efficient exploration of parameter spacesCode0
Generative AI and Creativity: A Systematic Literature Review and Meta-AnalysisCode0
Improved Benthic Classification using Resolution Scaling and SymmNet Unsupervised Domain AdaptationCode0
Language-Driven Active Learning for Diverse Open-Set 3D Object DetectionCode0
Local Padding in Patch-Based GANs for Seamless Infinite-Sized Texture SynthesisCode0
BLESS: Benchmarking Large Language Models on Sentence SimplificationCode0
Generating Informative and Diverse Conversational Responses via Adversarial Information MaximizationCode0
Generating Language Corrections for Teaching Physical Control TasksCode0
Blastocoel morphogenesis: a biophysics perspectiveCode0
Generating Diverse and Meaningful CaptionsCode0
Generating Diverse and Accurate Visual Captions by Comparative Adversarial LearningCode0
Generating Diverse and High-Quality Texts by Minimum Bayes Risk DecodingCode0
Generating Diverse Descriptions from Semantic GraphsCode0
Generating Natural Language Adversarial ExamplesCode0
Black-Box Testing of Deep Neural Networks Through Test Case DiversityCode0
Generating Automatically Print/Scan Textures for Morphing Attack Detection ApplicationsCode0
Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in HanabiCode0
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