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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 4451–4475 of 9051 papers

TitleStatusHype
Prompting Audios Using Acoustic Properties For Emotion Representation—0
Exploring Model Learning Heterogeneity for Boosting Ensemble RobustnessCode0
Beyond-Accuracy: A Review on Diversity, Serendipity and Fairness in Recommender Systems Based on Graph Neural Networks—0
Leveraging Diffusion Disentangled Representations to Mitigate Shortcuts in Underspecified Visual Tasks—0
Hierarchical Generation of Human-Object Interactions with Diffusion Probabilistic Models—0
Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks—0
No Offense Taken: Eliciting Offensiveness from Language ModelsCode0
Enhancing ML-Based DoS Attack Detection Through Combinatorial Fusion Analysis—0
LiveChat: Video Comment Generation from Audio-Visual Multimodal Contexts—0
A Comparative Study of Training Objectives for Clarification Facet GenerationCode0
An Experimental Prototype for Multistatic Asynchronous ISAC—0
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
ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill DiscoveryCode0
Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection—0
Towards a Unified Framework for Adaptable Problematic Content Detection via Continual LearningCode0
Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems—0
Post-Training Overfitting Mitigation in DNN Classifiers—0
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling—0
Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy MaximizationCode0
SJTU-TMQA: A quality assessment database for static mesh with texture map—0
Adaptive Priority Mechanisms—0
HPL-ViT: A Unified Perception Framework for Heterogeneous Parallel LiDARs in V2V—0
Facilitating Interdisciplinary Knowledge Transfer with Research Paper Recommender SystemsCode0
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