SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1476–1500 of 9051 papers

TitleStatusHype
ResT: An Efficient Transformer for Visual RecognitionCode1
AP-10K: A Benchmark for Animal Pose Estimation in the WildCode1
Class-Balancing Diffusion ModelsCode1
Rethinking Fano's Inequality in Ensemble LearningCode1
Re-thinking Federated Active Learning based on Inter-class DiversityCode1
EnvEdit: Environment Editing for Vision-and-Language NavigationCode1
Entropy Minimization vs. Diversity Maximization for Domain AdaptationCode1
Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language ModelsCode1
EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text ClassificationCode1
Class-Aware Mask-Guided Feature Refinement for Scene Text RecognitionCode1
Rethinking Parameter Counting in Deep Models: Effective Dimensionality RevisitedCode1
Evaluating Logical Generalization in Graph Neural NetworksCode1
Everyone Deserves A Reward: Learning Customized Human PreferencesCode1
Improving Adversarial Transferability with Gradient RefiningCode1
Euler-Lagrange Analysis of Generative Adversarial NetworksCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
Improving Contrastive Learning on Imbalanced Seed Data via Open-World SamplingCode1
RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domainsCode1
Accurate and Efficient Structural Ensemble Generation of Macrocyclic Peptides using Internal Coordinate DiffusionCode1
Improving Contrastive Learning on Imbalanced Data via Open-World SamplingCode1
Evaluating the Evaluation of Diversity in Natural Language GenerationCode1
Unconstrained Face-Mask & Face-Hand Datasets: Building a Computer Vision System to Help Prevent the Transmission of COVID-19Code1
Evaluation and Efficiency Comparison of Evolutionary Algorithms for Service Placement Optimization in Fog ArchitecturesCode1
Automatic Data Augmentation for 3D Medical Image SegmentationCode1
CityPersons: A Diverse Dataset for Pedestrian DetectionCode1
Show:102550
← PrevPage 60 of 363Next →

No leaderboard results yet.