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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 2401–2425 of 9051 papers

TitleStatusHype
Generating Realistic Forehead-Creases for User Verification via Conditioned Piecewise Polynomial CurvesCode0
BA-LoRA: Bias-Alleviating Low-Rank Adaptation to Mitigate Catastrophic Inheritance in Large Language ModelsCode0
Generating Language Corrections for Teaching Physical Control TasksCode0
3D2M Dataset: A 3-Dimension diverse Mesh DatasetCode0
Generating Natural Language Adversarial ExamplesCode0
A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source LayoutCode0
Beyond Task Diversity: Provable Representation Transfer for Sequential Multi-Task Linear BanditsCode0
Local Padding in Patch-Based GANs for Seamless Infinite-Sized Texture SynthesisCode0
Generating Diverse Descriptions from Semantic GraphsCode0
Generating Informative and Diverse Conversational Responses via Adversarial Information MaximizationCode0
Generating Neural Networks with Neural NetworksCode0
Toward Improving Coherence and Diversity of Slogan GenerationCode0
Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in HanabiCode0
Generating Automatically Print/Scan Textures for Morphing Attack Detection ApplicationsCode0
Generating Diverse and Accurate Visual Captions by Comparative Adversarial LearningCode0
Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?Code0
An Empirical Study of Translation Hypothesis Ensembling with Large Language ModelsCode0
A class of modular and flexible covariate-based covariance functions for nonstationary spatial modelingCode0
Generate then Refine: Data Augmentation for Zero-shot Intent DetectionCode0
Generating Diverse and High-Quality Texts by Minimum Bayes Risk DecodingCode0
Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer SatisfactionCode0
Generalized Dice Focal Loss trained 3D Residual UNet for Automated Lesion Segmentation in Whole-Body FDG PET/CT ImagesCode0
Generalizable Task Representation Learning for Offline Meta-Reinforcement Learning with Data LimitationsCode0
A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANsCode0
Generalized Face Anti-spoofing via Finer Domain Partition and Disentangling Liveness-irrelevant FactorsCode0
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