SOTAVerified

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 12511260 of 9051 papers

TitleStatusHype
Forcing Diffuse Distributions out of Language ModelsCode1
Forecasting Future World Events with Neural NetworksCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Amortizing intractable inference in large language modelsCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
AutoMix: Automatically Mixing Language ModelsCode1
FreEformer: Frequency Enhanced Transformer for Multivariate Time Series ForecastingCode1
Frequency Domain Model Augmentation for Adversarial AttackCode1
Automating Rigid Origami DesignCode1
Continual Object Detection via Prototypical Task Correlation Guided Gating MechanismCode1
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