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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 8926–8950 of 9051 papers

TitleStatusHype
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy MethodsCode0
Robustness and Diversity Seeking Data-Free Knowledge DistillationCode0
QUBE: Enhancing Automatic Heuristic Design via Quality-Uncertainty Balanced EvolutionCode0
An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment ClassificationCode0
Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness RewardCode0
On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven AnalysisCode0
On Practical Diversified Recommendation with Controllable Category Diversity FrameworkCode0
Evaluating Creative Short Story Generation in Humans and Large Language ModelsCode0
Aura: Privacy-preserving Augmentation to Improve Test Set Diversity in Speech EnhancementCode0
On Provable Length and Compositional GeneralizationCode0
On Redundancy and Diversity in Cell-based Neural Architecture SearchCode0
Supervised Uncertainty Quantification for Segmentation with Multiple AnnotationsCode0
Supervision policies can shape long-term risk management in general-purpose AI modelsCode0
SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean DiscrepancyCode0
Evaluating Coherence in Dialogue Systems using EntailmentCode0
RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution SamplesCode0
Topic-based Evaluation for Conversational BotsCode0
A Unified Theory of Diversity in Ensemble LearningCode0
Evaluating and Improving Graph to Text Generation with Large Language ModelsCode0
UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in RecommendationCode0
On the Complementarity between Pre-Training and Random-Initialization for Resource-Rich Machine TranslationCode0
An Axiomatic Analysis of Diversity Evaluation Metrics: Introducing the Rank-Biased Utility MetricCode0
RONA: Pragmatically Diverse Image Captioning with Coherence RelationsCode0
Incorporating Surprisingly Popular Algorithm and Euclidean Distance-based Adaptive Topology into PSOCode0
Surrogate-assisted evolutionary framework with an ensemble of teaching-learning and differential evolution for expensive optimizationCode0
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