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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 576–600 of 9051 papers

TitleStatusHype
Curriculum-guided Hindsight Experience ReplayCode1
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-SpeechCode1
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised SegmentationCode1
CrowdHuman: A Benchmark for Detecting Human in a CrowdCode1
Advanced Codebook Design for SCMA-aided NTNs With Randomly Distributed UsersCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data PruningCode1
A Doubly Decoupled Network for edge detectionCode1
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
Covariance Matrix Adaptation for the Rapid Illumination of Behavior SpaceCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context DemonstrationsCode1
COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse LearningCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
Controllable Video Captioning with an Exemplar SentenceCode1
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
An Extensible Benchmark Suite for Learning to Simulate Physical SystemsCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT ImagesCode1
dacl10k: Benchmark for Semantic Bridge Damage SegmentationCode1
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