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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

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Showing 8826–8850 of 9051 papers

TitleStatusHype
Exploring Diversity in Back Translation for Low-Resource Machine TranslationCode0
A*3D Dataset: Towards Autonomous Driving in Challenging EnvironmentsCode0
DexDeepFM: Ensemble Diversity Enhanced Extreme Deep Factorization Machine ModelCode0
Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous DrivingCode0
Exploratory State Representation LearningCode0
NusaBERT: Teaching IndoBERT to be Multilingual and MulticulturalCode0
Exploiting ConvNet Diversity for Flooding IdentificationCode0
Explaining crime diversity with Google street viewCode0
A Deep Generative Artificial Intelligence system to decipher species coexistence patternsCode0
Twin Auxilary Classifiers GANCode0
Carbohydrate NMR chemical shift predictions using E(3) equivariant graph neural networksCode0
Capturing the diversity of multilingual societiesCode0
ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist ExaminationCode0
Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible TemplatesCode0
Twin Auxiliary Classifiers GANCode0
Expanding functional protein sequence space using generative adversarial networksCode0
Privacy-preserving datasets by capturing feature distributions with Conditional VAEsCode0
Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series SummarizationCode0
Style Outweighs Substance: Failure Modes of LLM Judges in Alignment BenchmarkingCode0
Style-Restricted GAN: Multi-Modal Translation with Style Restriction Using Generative Adversarial NetworksCode0
Exhaustive Exploitation of Nature-inspired Computation for Cancer Screening in an Ensemble MannerCode0
Developing parsimonious ensembles using predictor diversity within a reinforcement learning frameworkCode0
Determinantal Point Process as an alternative to NMSCode0
Can Users Detect Biases or Factual Errors in Generated Responses in Conversational Information-Seeking?Code0
Revealing the Shape of Genome Space via K-mer TopologyCode0
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