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
Farsighted Probabilistic Sampling: A General Strategy for Boosting Local Search MaxSAT SolversCode0
Expressivity of Parameterized and Data-driven Representations in Quality Diversity SearchCode0
DeepPath: A Reinforcement Learning Method for Knowledge Graph ReasoningCode0
Diversity vs. Recognizability: Human-like generalization in one-shot generative modelsCode0
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active LearningCode0
Diversity with Cooperation: Ensemble Methods for Few-Shot ClassificationCode0
DIVE: Towards Descriptive and Diverse Visual Commonsense GenerationCode0
A Novel Bio-Inspired Texture Descriptor based on Biodiversity and Taxonomic MeasuresCode0
DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing UnderstandingCode0
Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic SegmentationCode0
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