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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 1676–1700 of 9051 papers

TitleStatusHype
Abusive Language Recognition in RussianCode0
Integrating LLMs and Decision Transformers for Language Grounded Generative Quality-DiversityCode0
Integrating Present and Past in Unsupervised Continual LearningCode0
Interactive Image Segmentation With Latent DiversityCode0
InstaSynth: Opportunities and Challenges in Generating Synthetic Instagram Data with ChatGPT for Sponsored Content DetectionCode0
A Culturally-Aware Tool for Crowdworkers: Leveraging Chronemics to Support Diverse Work StylesCode0
Instance-wise Supervision-level Optimization in Active LearningCode0
tcrLM: a lightweight protein language model for predicting T cell receptor and epitope binding specificityCode0
InstaNAS: Instance-aware Neural Architecture SearchCode0
INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical ExamplesCode0
Interactive Neural Style Transfer with ArtistsCode0
Is ChatGPT A Good Keyphrase Generator? A Preliminary StudyCode0
Input-gradient space particle inference for neural network ensemblesCode0
A Systematic Review of Reproducibility Research in Natural Language ProcessingCode0
InsBank: Evolving Instruction Subset for Ongoing AlignmentCode0
Information-Seeking Decision Strategies Mitigate Risk in Dynamic, Uncertain EnvironmentsCode0
A Systematic Characterization of Sampling Algorithms for Open-ended Language GenerationCode0
Information-Theoretic Active Learning for Content-Based Image RetrievalCode0
Synthetic Hyperspectral Array Video Database with Applications to Cross-Spectral Reconstruction and Hyperspectral Video CodingCode0
In-Context Example Selection via Similarity Search Improves Low-Resource Machine TranslationCode0
INGB: Informed Nonlinear Granular Ball Oversampling Framework for Noisy Imbalanced ClassificationCode0
Insect Identification in the Wild: The AMI DatasetCode0
Influence Maximization in Hypergraphs using Multi-Objective Evolutionary AlgorithmsCode0
Asymptotic theory of in-context learning by linear attentionCode0
Inference of cell dynamics on perturbation data using adjoint sensitivityCode0
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