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 7976–8000 of 9051 papers

TitleStatusHype
Data-Aware Device Scheduling for Federated Edge Learning—0
Data center top of rack switch to multiple spine switches optical wireless uplinks—0
Data-Centric Long-Tailed Image Recognition—0
Data Collection and Labeling Techniques for Machine Learning—0
Data-dependent Gaussian Prior Objective for Language Generation—0
Data Dieting in GAN Training—0
Data Distributional Properties As Inductive Bias for Systematic Generalization—0
Data Diversity Matters for Robust Instruction Tuning—0
Data-driven Discovery of Biophysical T Cell Receptor Co-specificity Rules—0
Data-Driven Discovery of Functional Cell Types that Improve Models of Neural Activity—0
Data-driven geophysical forecasting: Simple, low-cost, and accurate baselines with kernel methods—0
Data-Driven Learning of a Union of Sparsifying Transforms Model for Blind Compressed Sensing—0
Techniques for supercharging academic writing with generative AI—0
Data-driven, PCFG-based and Pseudo-PCFG-based Models for Chinese Dependency Parsing—0
Data-Driven Soil Organic Carbon Sampling: Integrating Spectral Clustering with Conditioned Latin Hypercube Optimization—0
Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction—0
Adversarial Robustness through Dynamic Ensemble Learning—0
Data-Efficient Protein 3D Geometric Pretraining via Refinement of Diffused Protein Structure Decoy—0
Data Ethics in the Era of Healthcare Artificial Intelligence in Africa: An Ubuntu Philosophy Perspective—0
Data-Free Distillation of Language Model by Text-to-Text Transfer—0
Adversarial optimization leads to over-optimistic security-constrained dispatch, but sampling can help—0
Superpixel-based Color Transfer—0
Data-Free Neural Architecture Search via Recursive Label Calibration—0
Superposition-Assisted Stochastic Optimization for Hawkes Processes—0
Data Generation Using Large Language Models for Text Classification: An Empirical Case Study—0
Show:102550
← PrevPage 320 of 363Next →

No leaderboard results yet.