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 5051–5075 of 9051 papers

TitleStatusHype
Redrawing attendance boundaries to promote racial and ethnic diversity in elementary schools—0
Siamese Graph Learning for Semi-supervised Age EstimationCode0
ODIN: On-demand Data Formulation to Mitigate Dataset Lock-in—0
Nearest-Neighbor Inter-Intra Contrastive Learning from Unlabeled Videos—0
Architext: Language-Driven Generative Architecture Design—0
Compressed Heterogeneous Graph for Abstractive Multi-Document SummarizationCode0
One Neuron Saved Is One Neuron Earned: On Parametric Efficiency of Quadratic NetworksCode0
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image—0
Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning—0
Unifying Layout Generation with a Decoupled Diffusion Model—0
Knowledge-augmented Few-shot Visual Relation Detection—0
An Unscented Kalman Filter-Informed Neural Network for Vehicle Sideslip Angle Estimation—0
The evolution of cooperation and diversity by integrated indirect reciprocity—0
Interpretable Visual Question Answering Referring to Outside Knowledge—0
Deep Occupancy-Predictive Representations for Autonomous Driving—0
Bias, diversity, and challenges to fairness in classification and automated text analysis. From libraries to AI and back—0
Lformer: Text-to-Image Generation with L-shape Block Parallel Decoding—0
Controlled Diversity with Preference : Towards Learning a Diverse Set of Desired SkillsCode0
To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer LearningCode0
Emergent competition shapes the ecological properties of multi-trophic ecosystems—0
Mining both Commonality and Specificity from Multiple Documents for Multi-Document Summarization—0
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output CodesCode0
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing SymmetriesCode0
Data Augmentation for Generating Synthetic Electrogastrogram Time SeriesCode0
Effective Visualization and Analysis of Recommender Systems—0
Show:102550
← PrevPage 203 of 363Next →

No leaderboard results yet.