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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 1726–1750 of 9051 papers

TitleStatusHype
AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation—0
Abstractive and mixed summarization for long-single documents—0
Attacking Transformers with Feature Diversity Adversarial Perturbation—0
A trust-based recommendation method using network diffusion processes—0
Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder—0
A tool for computing diversity and consideration on differences between diversity indices—0
Atom Responding Machine for Dialog Generation—0
Data Augmentation in Earth Observation: A Diffusion Model Approach—0
Dynamic Latent Separation for Deep Learning—0
ATM-R: An Adaptive Tradeoff Model with Reference Points for Constrained Multiobjective Evolutionary Optimization—0
A Large-Scale 3D Face Mesh Video Dataset via Neural Re-parameterized Optimization—0
A Time Series Analysis-Based Forecasting Framework for the Indian Healthcare Sector—0
Achieving Tight O(4^k) Runtime Bounds on Jump_k by Proving that Genetic Algorithms Evolve Near-Maximal Population Diversity—0
Activitynet 2019 Task 3: Exploring Contexts for Dense Captioning Events in Videos—0
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks—0
A Large Language Model for Feasible and Diverse Population Synthesis—0
α-TCVAE: On the relationship between Disentanglement and Diversity—0
A Taxonomy of Adaptive Traffic Signal Control—0
A Large and Balanced Corpus for Fine-grained Arabic Readability Assessment—0
Data Augmentation for Seizure Prediction with Generative Diffusion Model—0
Data Augmentation with Adversarial Training for Cross-Lingual NLI—0
Data-Driven Discovery of Functional Cell Types that Improve Models of Neural Activity—0
A Tailored NSGA-III Instantiation for Flexible Job Shop Scheduling—0
A Systematic Survey on Deep Generative Models for Graph Generation—0
A K-means-based Multi-subpopulation Particle Swarm Optimization for Neural Network Ensemble—0
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