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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 311320 of 9051 papers

TitleStatusHype
AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task GenerationCode1
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-SpeechCode1
Curriculum-guided Hindsight Experience ReplayCode1
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic ModelsCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
Adaptable Agent Populations via a Generative Model of PoliciesCode1
COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT ImagesCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
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