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 8051–8075 of 9051 papers

TitleStatusHype
Decoupling Shape and Density for Liver Lesion Synthesis Using Conditional Generative Adversarial Networks—0
Cooperation guides evolution in a minimal model of biological evolution—0
Deep Active Learning for Multi-Label Classification of Remote Sensing Images—0
Does Deep Active Learning Work in the Wild?—0
Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient—0
Deep Active Learning for Text Classification with Diverse Interpretations—0
Deep Active Learning in the Open World—0
Deep Active Learning in the Presence of Label Noise: A Survey—0
Visual Sensation and Perception Computational Models for Deep Learning: State of the art, Challenges and Prospects—0
Deep Architectures and Ensembles for Semantic Video Classification—0
COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting—0
Deep Billboards towards Lossless Real2Sim in Virtual Reality—0
Convolutional autoencoder-based multimodal one-class classification—0
Deep Complementary Joint Model for Complex Scene Registration and Few-shot Segmentation on Medical Images—0
Deep Concept Identification for Generative Design—0
Deep Convolutional Compression for Massive MIMO CSI Feedback—0
VisualToolAgent (VisTA): A Reinforcement Learning Framework for Visual Tool Selection—0
Deep Delay Loop Reservoir Computing for Specific Emitter Identification—0
Deep Determinantal Point Processes—0
Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning—0
Deep Dynamic Neural Network to trade-off between Accuracy and Diversity in a News Recommender System—0
Conversational AI-Powered Design: ChatGPT as Designer, User, and Product—0
Deep Ensemble Collaborative Learning by using Knowledge-transfer Graph for Fine-grained Object Classification—0
Deep Ensemble Policy Learning—0
Deep Ensembles for Low-Data Transfer Learning—0
Show:102550
← PrevPage 323 of 363Next →

No leaderboard results yet.