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 28012810 of 9051 papers

TitleStatusHype
Geometry-Informed Neural NetworksCode2
Class-Aware Mask-Guided Feature Refinement for Scene Text RecognitionCode1
A Multimodal In-Context Tuning Approach for E-Commerce Product Description GenerationCode1
WhaleNet: a Novel Deep Learning Architecture for Marine Mammals Vocalizations on Watkins Marine Mammal Sound DatabaseCode0
Robust Model Predictive Control for nonlinear discrete-time systems using iterative time-varying constraint tightening0
GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trickCode0
wmh_seg: Transformer based U-Net for Robust and Automatic White Matter Hyperintensity Segmentation across 1.5T, 3T and 7TCode1
NeRF Solves Undersampled MRI Reconstruction0
Evolving AI Collectives to Enhance Human Diversity and Enable Self-Regulation0
Parallel Structures in Pre-training Data Yield In-Context LearningCode0
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