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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4591–4600 of 9051 papers

TitleStatusHype
Pairwise Instance Relation Augmentation for Long-tailed Multi-label Text Classification—0
Combining State-of-the-Art Models with Maximal Marginal Relevance for Few-Shot and Zero-Shot Multi-Document Summarization—0
Social Diversity Reduces the Complexity and Cost of Fostering Fairness—0
Weighted Ensemble Self-Supervised Learning—0
A Structure-Guided Diffusion Model for Large-Hole Image CompletionCode0
Vision Transformers in Medical Imaging: A Review—0
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
DGRec: Graph Neural Network for Recommendation with Diversified Embedding GenerationCode1
Delving into Transformer for Incremental Semantic Segmentation—0
UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot SummarizationCode1
Show:102550
← PrevPage 460 of 906Next →

No leaderboard results yet.