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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

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Showing 3476–3500 of 9051 papers

TitleStatusHype
An Experimental Prototype for Multistatic Asynchronous ISAC—0
A Comparative Study of Training Objectives for Clarification Facet GenerationCode0
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI ModelsCode0
Finding Pragmatic Differences Between Disciplines—0
Evolving Diverse Red-team Language Models in Multi-round Multi-agent Games—0
Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection—0
ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill DiscoveryCode0
Towards a Unified Framework for Adaptable Problematic Content Detection via Continual LearningCode0
Post-Training Overfitting Mitigation in DNN Classifiers—0
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling—0
Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems—0
HPL-ViT: A Unified Perception Framework for Heterogeneous Parallel LiDARs in V2V—0
SJTU-TMQA: A quality assessment database for static mesh with texture map—0
Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy MaximizationCode0
Adaptive Priority Mechanisms—0
Boosting High Resolution Image Classification with Scaling-up TransformersCode0
Beauty beacon: correlated strategies for the Fisher runaway process—0
Structure Invariant Transformation for better Adversarial TransferabilityCode1
Facilitating Interdisciplinary Knowledge Transfer with Research Paper Recommender SystemsCode0
DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch—0
KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse TransformationCode1
Exploring Robot Morphology Spaces through Breadth-First Search and Random Query—0
Convolutional autoencoder-based multimodal one-class classification—0
Dual Feature Augmentation Network for Generalized Zero-shot LearningCode1
Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience—0
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