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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 2591–2600 of 9051 papers

TitleStatusHype
Benchmarking and Improving Text-to-SQL Generation under AmbiguityCode0
Developing parsimonious ensembles using predictor diversity within a reinforcement learning frameworkCode0
Boosting Ensemble Accuracy by Revisiting Ensemble Diversity MetricsCode0
Diverse Plausible Shape Completions from Ambiguous Depth ImagesCode0
Diverse Policies Converge in Reward-free Markov Decision ProcesseCode0
First U-Net Layers Contain More Domain Specific Information Than The Last OnesCode0
Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and FlatnessCode0
Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field SamplingCode0
Forest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target ImputationCode0
Analyzing the Habitable Zones of Circumbinary Planets Using Machine LearningCode0
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