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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 1701–1725 of 9051 papers

TitleStatusHype
Instance-wise Supervision-level Optimization in Active LearningCode0
AILS-NTUA at SemEval-2025 Task 8: Language-to-Code prompting and Error Fixing for Tabular Question AnsweringCode0
InstaNAS: Instance-aware Neural Architecture SearchCode0
A Brief Study on the Effects of Training Generative Dialogue Models with a Semantic lossCode0
InstaSynth: Opportunities and Challenges in Generating Synthetic Instagram Data with ChatGPT for Sponsored Content DetectionCode0
Intent Factored Generation: Unleashing the Diversity in Your Language ModelCode0
Insect Identification in the Wild: The AMI DatasetCode0
InsBank: Evolving Instruction Subset for Ongoing AlignmentCode0
Input-gradient space particle inference for neural network ensemblesCode0
Information-Theoretic Active Learning for Content-Based Image RetrievalCode0
AI-enhanced Collective IntelligenceCode0
INGB: Informed Nonlinear Granular Ball Oversampling Framework for Noisy Imbalanced ClassificationCode0
Result Diversification in Search and Recommendation: A SurveyCode0
A Survey of Data Synthesis ApproachesCode0
About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data AnnotationsCode0
INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical ExamplesCode0
InfoDiffusion: Information Entropy Aware Diffusion Process for Non-Autoregressive Text GenerationCode0
Information Density Principle for MLLM BenchmarksCode0
Inference of cell dynamics on perturbation data using adjoint sensitivityCode0
Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic RegressionCode0
Influence Maximization in Hypergraphs using Multi-Objective Evolutionary AlgorithmsCode0
A Structure-Guided Diffusion Model for Large-Hole Image CompletionCode0
Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic ManuscriptsCode0
In-distribution Public Data Synthesis with Diffusion Models for Differentially Private Image ClassificationCode0
Information-Seeking Decision Strategies Mitigate Risk in Dynamic, Uncertain EnvironmentsCode0
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