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 821–830 of 9051 papers

TitleStatusHype
Efficient Dataset Distillation via Minimax DiffusionCode1
Improving Integrated Gradient-based Transferable Adversarial Examples by Refining the Integration PathCode1
Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural NetworksCode1
ARGS: Alignment as Reward-Guided SearchCode1
Argumentative Large Language Models for Explainable and Contestable Claim VerificationCode1
DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector QuantizationCode1
Effect of latent space distribution on the segmentation of images with multiple annotationsCode1
AutoSTR: Efficient Backbone Search for Scene Text RecognitionCode1
Grounding Language to Autonomously-Acquired Skills via Goal GenerationCode1
Efficient Neural Neighborhood Search for Pickup and Delivery ProblemsCode1
Show:102550
← PrevPage 83 of 906Next →

No leaderboard results yet.