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 15611570 of 9051 papers

TitleStatusHype
Reinforcement learning on structure-conditioned categorical diffusion for protein inverse foldingCode1
Reinforcement Learning with Convex ConstraintsCode1
AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand PoseCode1
ReLER@ZJU-Alibaba Submission to the Ego4D Natural Language Queries Challenge 2022Code1
Clotho: An Audio Captioning DatasetCode1
CloudEval-YAML: A Practical Benchmark for Cloud Configuration GenerationCode1
COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse LearningCode1
Reprogramming Pretrained Language Models for Antibody Sequence InfillingCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic ModelsCode1
Show:102550
← PrevPage 157 of 906Next →

No leaderboard results yet.