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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 2576–2600 of 9051 papers

TitleStatusHype
Benchmarking histopathology foundation models in a multi-center dataset for skin cancer subtypingCode0
BOLD5000: A public fMRI dataset of 5000 imagesCode0
Increasing diversity of omni-directional images generated from single image using cGAN based on MLPMixerCode0
Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through OptionsCode0
Indian Regional Movie Dataset for Recommender SystemsCode0
DFPE: A Diverse Fingerprint Ensemble for Enhancing LLM PerformanceCode0
Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic ManuscriptsCode0
Flow-Grounded Spatial-Temporal Video Prediction from Still ImagesCode0
Analyzing Uncertainty in Neural Machine TranslationCode0
Influence Maximization in Hypergraphs using Multi-Objective Evolutionary AlgorithmsCode0
DexDeepFM: Ensemble Diversity Enhanced Extreme Deep Factorization Machine ModelCode0
Flickr-PAD: New Face High-Resolution Presentation Attack Detection DatabaseCode0
Flexible Modeling of Diversity with Strongly Log-Concave DistributionsCode0
Boosting Deep Ensemble Performance with Hierarchical PruningCode0
Information-Theoretic Active Learning for Content-Based Image RetrievalCode0
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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