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 4976–5000 of 9051 papers

TitleStatusHype
Diversity-aware social robots meet people: beyond context-aware embodied AI—0
Causal Conceptions of Fairness and their Consequences—0
IDEA: Increasing Text Diversity via Online Multi-Label Recognition for Vision-Language Pre-trainingCode0
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy OptimizationCode1
Frequency Domain Model Augmentation for Adversarial AttackCode1
Origin of power laws and their spatial fractal structure for city-size distributions—0
Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?—0
Fine-grained Activities of People Worldwide—0
Multimodal Multi-objective Optimization: Comparative Study of the State-of-the-ArtCode1
SummScore: A Comprehensive Evaluation Metric for Summary Quality Based on Cross-Encoder—0
Learning an evolved mixture model for task-free continual learning—0
Interaction Pattern Disentangling for Multi-Agent Reinforcement LearningCode1
PoseGU: 3D Human Pose Estimation with Novel Human Pose Generator and Unbiased Learning—0
Exploring Generative Adversarial Networks for Text-to-Image Generation with Evolution StrategiesCode0
Learning to Diversify for Product Question Generation—0
Mitigating shortage of labeled data using clustering-based active learning with diversity explorationCode0
Quantitative Assessment of DESIS Hyperspectral Data for Plant Biodiversity Estimation in Australia—0
DBN-Mix: Training Dual Branch Network Using Bilateral Mixup Augmentation for Long-Tailed Visual Recognition—0
PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch—0
Vision-and-Language PretrainingCode0
Accelerating Score-based Generative Models with Preconditioned Diffusion SamplingCode1
T-DominO: Exploring Multiple Criteria with Quality-Diversity and the Tournament Dominance ObjectiveCode0
Recommendation Systems with Distribution-Free Reliability Guarantees—0
Anomaly-aware multiple instance learning for rare anemia disorder classificationCode0
Selectively increasing the diversity of GAN-generated samples—0
Show:102550
← PrevPage 200 of 363Next →

No leaderboard results yet.