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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 9001–9025 of 9051 papers

TitleStatusHype
Bridging Information Gaps with Comprehensive Answers: Improving the Diversity and Informativeness of Follow-Up QuestionsCode0
Deep Active Learning: Unified and Principled Method for Query and TrainingCode0
Unbiased estimation of sampling variance for Simpson's diversity indexCode0
Deconditional Downscaling with Gaussian ProcessesCode0
On the Universal Truthfulness Hyperplane Inside LLMsCode0
On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution DetectionCode0
Does In-Context Learning Really Learn? Rethinking How Large Language Models Respond and Solve Tasks via In-Context LearningCode0
Uncertain Quality-Diversity: Evaluation methodology and new methods for Quality-Diversity in Uncertain DomainsCode0
Decomposed Distribution Matching in Dataset CondensationCode0
Decoding MIE: A Novel Dataset Approach Using Topic Extraction and Affiliation ParsingCode0
Zero-shot racially balanced dataset generation using an existing biased StyleGAN2Code0
TourLLM: Enhancing LLMs with Tourism KnowledgeCode0
Visual-Semantic Decomposition and Partial Alignment for Document-based Zero-Shot LearningCode0
Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural NetworksCode0
When SparseMoE Meets Noisy Interactions: An Ensemble View on Denoising RecommendationCode0
Topology-Preserved Human Reconstruction with DetailsCode0
Open-Domain Question-Answering for COVID-19 and Other Emergent DomainsCode0
Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep ClassificationCode0
Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data SilosCode0
Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary AlgorithmsCode0
In-Context Example Selection via Similarity Search Improves Low-Resource Machine TranslationCode0
OpenEthics: A Comprehensive Ethical Evaluation of Open-Source Generative Large Language ModelsCode0
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials DataCode0
Synergizing Quality-Diversity with Descriptor-Conditioned Reinforcement LearningCode0
Open Heterogeneous Data for Condition Monitoring of Multi Faults in Rotating Machines Used in Different Operating ConditionsCode0
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