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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

Showing 28512875 of 9051 papers

TitleStatusHype
An Active Learning Approach for Reducing Annotation Cost in Skin Lesion AnalysisCode0
Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental ComparisonsCode0
Capturing the diversity of multilingual societiesCode0
Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing dataCode0
Facts That MatterCode0
Exploring the Role of Diversity in Example Selection for In-Context LearningCode0
Exploring the Performance-Reproducibility Trade-off in Quality-DiversityCode0
Carbohydrate NMR chemical shift predictions using E(3) equivariant graph neural networksCode0
Exploring the Role of Node Diversity in Directed Graph Representation LearningCode0
Exploring the Evolution of GANs through Quality DiversityCode0
Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic SegmentationCode0
DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited DataCode0
Exploring Sparsity for Parameter Efficient Fine Tuning Using WaveletsCode0
Exploring Model Learning Heterogeneity for Boosting Ensemble RobustnessCode0
Exploring Precision and Recall to assess the quality and diversity of LLMsCode0
MURI: High-Quality Instruction Tuning Datasets for Low-Resource Languages via Reverse InstructionsCode0
Exploring the Capabilities of Large Language Models for Generating Diverse Design SolutionsCode0
Mutual Information and Diverse Decoding Improve Neural Machine TranslationCode0
Fair and Diverse DPP-based Data SummarizationCode0
Named Entity Recognition With Parallel Recurrent Neural NetworksCode0
Exploring Format Consistency for Instruction TuningCode0
Deep Hashing with Category Mask for Fast Video RetrievalCode0
DOS: Diverse Outlier Sampling for Out-of-Distribution DetectionCode0
Exploring Generative Adversarial Networks for Text-to-Image Generation with Evolution StrategiesCode0
Exploring Flat Minima for Domain Generalization with Large Learning RatesCode0
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