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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 2826–2850 of 9051 papers

TitleStatusHype
Unveiling the Mystery of Visual Attributes of Concrete and Abstract Concepts: Variability, Nearest Neighbors, and Challenging CategoriesCode0
Enhancing Assamese NLP Capabilities: Introducing a Centralized Dataset RepositoryCode0
Hairmony: Fairness-aware hairstyle classification—0
Reading Yule in light of the history and present of macroevolution—0
YOLO-ELA: Efficient Local Attention Modeling for High-Performance Real-Time Insulator Defect Detection—0
Dual-Teacher Ensemble Models with Double-Copy-Paste for 3D Semi-Supervised Medical Image SegmentationCode0
PSVMA+: Exploring Multi-granularity Semantic-visual Adaption for Generalized Zero-shot Learning—0
Improving Bias in Facial Attribute Classification: A Combined Impact of KL Divergence induced Loss Function and Dual Attention—0
Experiences from Creating a Benchmark for Sentiment Classification for Varieties of English—0
Personas with Attitudes: Controlling LLMs for Diverse Data AnnotationCode0
Understanding Likelihood Over-optimisation in Direct Alignment Algorithms—0
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks—0
FedCCRL: Federated Domain Generalization with Cross-Client Representation LearningCode0
DDIL: Diversity Enhancing Diffusion Distillation With Imitation Learning—0
Automated Filtering of Human Feedback Data for Aligning Text-to-Image Diffusion ModelsCode0
Cultural Heritage 3D Reconstruction with Diffusion NetworksCode0
Class Balancing Diversity Multimodal Ensemble for Alzheimer's Disease Diagnosis and Early Detection—0
Diversity-Aware Reinforcement Learning for de novo Drug Design—0
3D-Prover: Diversity Driven Theorem Proving With Determinantal Point Processes—0
DrivingDojo Dataset: Advancing Interactive and Knowledge-Enriched Driving World Model—0
Single Ground Truth Is Not Enough: Add Linguistic Variability to Aspect-based Sentiment Analysis Evaluation—0
Towards the Synthesis of Non-speech Vocalizations—0
VLFeedback: A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment—0
Hybrid Training Approaches for LLMs: Leveraging Real and Synthetic Data to Enhance Model Performance in Domain-Specific Applications—0
AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion—0
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