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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 1551–1575 of 9051 papers

TitleStatusHype
Covariance Matrix Adaptation for the Rapid Illumination of Behavior SpaceCode1
CoDEPS: Online Continual Learning for Depth Estimation and Panoptic SegmentationCode1
REAL Sampling: Boosting Factuality and Diversity of Open-Ended Generation via Asymptotic EntropyCode1
Real-World Image Super-Resolution by Exclusionary Dual-LearningCode1
A Quantum Leaky Integrate-and-Fire Spiking Neuron and NetworkCode1
Recommendations for Item Set Completion: On the Semantics of Item Co-Occurrence With Data Sparsity, Input Size, and Input ModalitiesCode1
Red Teaming Language Models with Language ModelsCode1
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual GroundingCode1
Reinforcement learning on structure-conditioned categorical diffusion for protein inverse foldingCode1
CLoG: Benchmarking Continual Learning of Image Generation ModelsCode1
Reinforcement Recommendation Reasoning through Knowledge Graphs for Explanation Path QualityCode1
Relation-Guided Adversarial Learning for Data-free Knowledge TransferCode1
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text GenerationCode1
Remastering Divide and Remaster: A Cinematic Audio Source Separation Dataset with Multilingual SupportCode1
Clotho: An Audio Captioning DatasetCode1
CloudEval-YAML: A Practical Benchmark for Cloud Configuration GenerationCode1
CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context DemonstrationsCode1
Repulsive Deep Ensembles are BayesianCode1
Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysisCode1
ReSmooth: Detecting and Utilizing OOD Samples when Training with Data AugmentationCode1
COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse LearningCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
ResViT: Residual vision transformers for multi-modal medical image synthesisCode1
Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image SegmentationCode1
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic ModelsCode1
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