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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

Showing 591600 of 9051 papers

TitleStatusHype
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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