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 491500 of 9051 papers

TitleStatusHype
Ego2Hands: A Dataset for Egocentric Two-hand Segmentation and DetectionCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
Design of Chain-of-Thought in Math Problem SolvingCode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
A View From Somewhere: Human-Centric Face RepresentationsCode1
CETN: Contrast-enhanced Through Network for CTR PredictionCode1
ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-Tree Complex Wavelet Representation and Contradict Channel LossCode1
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving PlannersCode1
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense EvaluationCode1
Determinantal Point Process Likelihoods for Sequential RecommendationCode1
Show:102550
← PrevPage 50 of 906Next →

No leaderboard results yet.