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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 30413050 of 9051 papers

TitleStatusHype
A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide GenerationCode1
Learning from diversity: jati fractionalization, social expectations and improved sanitation practices in India0
TimePillars: Temporally-Recurrent 3D LiDAR Object Detection0
Cross-Covariate Gait Recognition: A BenchmarkCode1
Quality-Diversity Generative Sampling for Learning with Synthetic DataCode1
Efficacy of Machine-Generated Instructions0
De novo Drug Design using Reinforcement Learning with Multiple GPT AgentsCode1
Q-SENN: Quantized Self-Explaining Neural NetworksCode1
ChatGPT as a commenter to the news: can LLMs generate human-like opinions?Code0
Navigating the Structured What-If Spaces: Counterfactual Generation via Structured Diffusion0
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