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

Showing 27012710 of 9051 papers

TitleStatusHype
Flow-Grounded Spatial-Temporal Video Prediction from Still ImagesCode0
Bayesian Renewables Scenario Generation via Deep Generative NetworksCode0
Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parametersCode0
BSDA: Bayesian Random Semantic Data Augmentation for Medical Image ClassificationCode0
Feasible Recourse Plan via Diverse InterpolationCode0
LEATHER: A Framework for Learning to Generate Human-like Text in DialogueCode0
Feature Space Particle Inference for Neural Network EnsemblesCode0
LENAS: Learning-based Neural Architecture Search and Ensemble for 3D Radiotherapy Dose PredictionCode0
Bayesian Prediction of Future Street Scenes using Synthetic LikelihoodsCode0
Fast Texture Synthesis via Pseudo OptimizerCode0
Show:102550
← PrevPage 271 of 906Next →

No leaderboard results yet.