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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 5926–5950 of 9051 papers

TitleStatusHype
Network Flow Based Post Processing for Sales Diversity—0
Network Inversion and Its Applications—0
Network Inversion of Convolutional Neural Nets—0
Network of Evolvable Neural Units: Evolving to Learn at a Synaptic Level—0
Network Slicing for eMBB and mMTC with NOMA and Space Diversity Reception—0
Network Traffic Anomaly Detection Method Based on Multi scale Residual Feature—0
Neural Architecture Search via Ensemble-based Knowledge Distillation—0
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling—0
Neural Categorical Priors for Physics-Based Character Control—0
Neural Cross-Domain Collaborative Filtering with Shared Entities—0
A Multi-level Acoustic Feature Extraction Framework for Transformer Based End-to-End Speech Recognition—0
Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model—0
The impact of behavioral diversity in multi-agent reinforcement learning—0
Neural-Driven Multi-criteria Tree Search for Paraphrase Generation—0
Assessing the Coherence Modeling Capabilities of Pretrained Transformer-based Language Models—0
Assessing Social Determinants-Related Performance Bias of Machine Learning Models: A case of Hyperchloremia Prediction in ICU Population—0
Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?—0
Assessing Quality-Diversity Neuro-Evolution Algorithms Performance in Hard Exploration Problems—0
Neural Machine Translation Data Generation and Augmentation using ChatGPT—0
Neural Machine Translation for Cebuano to Tagalog with Subword Unit Translation—0
When More Data Hurts: Optimizing Data Coverage While Mitigating Diversity Induced Underfitting in an Ultra-Fast Machine-Learned Potential—0
Assessing Intra-class Diversity and Quality of Synthetically Generated Images in a Biomedical and Non-biomedical Setting—0
Neural Network-Enhanced Disease Spread Dynamics Over Time and Space—0
Neural Network Ensembles: Theory, Training, and the Importance of Explicit Diversity—0
Typography Leads Semantic Diversifying: Amplifying Adversarial Transferability across Multimodal Large Language Models—0
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