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 19011910 of 9051 papers

TitleStatusHype
Attribute Alignment: Controlling Text Generation from Pre-trained Language ModelsCode0
LookHere: Vision Transformers with Directed Attention Generalize and ExtrapolateCode0
ABD-Net: Attentive but Diverse Person Re-IdentificationCode0
Improving Adversarial Robustness via Decoupled Visual Representation MaskingCode0
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQLCode0
Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-TuningCode0
Improved Generalization of Weight Space Networks via AugmentationsCode0
Improved Benthic Classification using Resolution Scaling and SymmNet Unsupervised Domain AdaptationCode0
Improved Generation of Synthetic Imaging Data Using Feature-Aligned DiffusionCode0
Importance of Search and Evaluation Strategies in Neural Dialogue ModelingCode0
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