Latest Articles for Data Science

Evaluating quantization and pruning to optimize DRL models for limited resources.

Introducing a method to improve sentiment extraction in text through latent dependency trees.

Exploring how neural networks use their learned weights effectively.

A method merging Gradient Boosting and Mixed Effects for precise area-level predictions.

A review of methods for assessing time-to-event predictions in data science.

Learn how VPR improves efficiency in Bayesian inference by updating prior information.

A novel approach enhances example retrieval for large language models.

A method to enhance audio deepfake detection through data augmentation.

New techniques use machine learning to predict galaxy properties based on dark matter halos.

New techniques for fast kernel matrix approximations using Chebyshev and tensor train methods.

Explore the impact of out-of-distribution data on machine learning performance.

LayerShuffle enhances the robustness of neural networks by enabling flexible layer execution.

RealMLP enhances machine learning efficiency for tabular data analysis.

A new framework boosts model performance with limited data.

A fresh approach enhances community detection in sparse and imbalanced networks.

Learn how to estimate entropy while managing limited memory resources.

Masked diffusion models show promise in generative modeling for text and images.

A new approach to improve prediction reliability in machine learning.

A new method improves the search for mathematical expressions from data.

Introducing a new method for Bayesian neural networks that improves uncertainty modeling.

Exploring the potential of KAHMs in federated learning for improved privacy and efficiency.

Examining how invariance impacts model performance in transfer learning.

This method improves data selection in Bayesian optimization, enhancing efficiency and outcomes.

New techniques improve training speeds for deep learning recommendation models.

Examining the role of dropout techniques in improving fairness in DNNs.

This article explores overparameterization and its impact on model training efficiency.

This study evaluates survival analysis techniques using simple datasets with missing data.

This study shows how ensemble methods enhance suggestion quality in recommender systems.

This paper explores ways to better assess model calibration and predictive accuracy.

A study on methods for predicting dengue cases using various statistical and machine learning techniques.

FlowLearn enhances flowchart comprehension for advanced models with scientific and simulated diagrams.

Discover how random subspaces improve model generalization in machine learning.

A new approach enhances the clarity of questions generated from images.

Machine learning techniques improve predictions of crystal structures from electron diffraction patterns.

Examining the progress and hurdles in processing Kazakh, Uzbek, Kyrgyz, and Turkmen languages.

Improving trust and compliance in language models through accurate source attribution.

Analyzing how data order affects memory in recurrent language models.

A new method generates synthetic data to improve detection of false outputs.

A new framework improves AI medical image classification by addressing biases and forgetting outdated information.

Introducing a model that combines tree-based transforms for effective outcome generation.