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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 726–750 of 9051 papers

TitleStatusHype
Color Space Learning for Cross-Color Person Re-IdentificationCode1
Co-Mixup: Saliency Guided Joint Mixup with Supermodular DiversityCode1
COMETA: A Corpus for Medical Entity Linking in the Social MediaCode1
CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph DiffusionCode1
Complex Evolutional Pattern Learning for Temporal Knowledge Graph ReasoningCode1
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human ProgrammersCode1
Forecasting Future World Events with Neural NetworksCode1
DRIT++: Diverse Image-to-Image Translation via Disentangled RepresentationsCode1
Any-Play: An Intrinsic Augmentation for Zero-Shot CoordinationCode1
An Empirical Investigation of Pre-Trained Transformer Language Models for Open-Domain Dialogue GenerationCode1
DriveDiTFit: Fine-tuning Diffusion Transformers for Autonomous DrivingCode1
Dual Feature Augmentation Network for Generalized Zero-shot LearningCode1
Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code ContributionsCode1
Compositional Feature Augmentation for Unbiased Scene Graph GenerationCode1
Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence LearningCode1
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural NetworksCode1
BenthicNet: A global compilation of seafloor images for deep learning applicationsCode1
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual ConceptsCode1
Ape210K: A Large-Scale and Template-Rich Dataset of Math Word ProblemsCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement LearningCode1
A Picture is Worth a Thousand Words: A Unified System for Diverse Captions and Rich Images GenerationCode1
Unconstrained Face-Mask & Face-Hand Datasets: Building a Computer Vision System to Help Prevent the Transmission of COVID-19Code1
DRA-GRPO: Exploring Diversity-Aware Reward Adjustment for R1-Zero-Like Training of Large Language ModelsCode1
Domain-specific ChatBots for Science using EmbeddingsCode1
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