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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 2326–2350 of 9051 papers

TitleStatusHype
GiantHunter: Accurate detection of giant virus in metagenomic data using reinforcement-learning and Monte Carlo tree searchCode0
Problematic Tokens: Tokenizer Bias in Large Language ModelsCode0
Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPUCode0
ADS-Cap: A Framework for Accurate and Diverse Stylized Captioning with Unpaired Stylistic CorporaCode0
BooVAE: Boosting Approach for Continual Learning of VAECode0
GenZSL: Generative Zero-Shot Learning Via Inductive Variational AutoencoderCode0
3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single ImageCode0
Bootstrapping and Multiple Imputation Ensemble Approaches for Missing DataCode0
Global News Synchrony and Diversity During the Start of the COVID-19 PandemicCode0
GRATIS: GeneRAting TIme Series with diverse and controllable characteristicsCode0
Generative Monoculture in Large Language ModelsCode0
Boosting Semantic Segmentation from the Perspective of Explicit Class EmbeddingsCode0
Boosting Out-of-Distribution Detection with Multiple Pre-trained ModelsCode0
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language ModelingCode0
Boosting LLM via Learning from Data Iteratively and SelectivelyCode0
Boosting High Resolution Image Classification with Scaling-up TransformersCode0
Generating Sentential Arguments from Diverse Perspectives on Controversial TopicCode0
Boosting Ensemble Accuracy by Revisiting Ensemble Diversity MetricsCode0
AdLER: Adversarial Training with Label Error Rectification for One-Shot Medical Image SegmentationCode0
Generating Realistic Forehead-Creases for User Verification via Conditioned Piecewise Polynomial CurvesCode0
Generative AI and Creativity: A Systematic Literature Review and Meta-AnalysisCode0
Genetic Algorithm with Innovative Chromosome Patterns in the Breeding ProcessCode0
Boosting Deep Ensemble Performance with Hierarchical PruningCode0
Generating Language Corrections for Teaching Physical Control TasksCode0
Local Padding in Patch-Based GANs for Seamless Infinite-Sized Texture SynthesisCode0
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