Latest Articles for Data Science

A look at how training methods affect model performance in machine learning.

A structured approach enhances financial language model performance through quality data.

Active learning enhances data training efficiency through strategic sample selection.

Ordinal methods reveal insights into complex network behaviors and interactions.

RS-CNN offers a new approach to quantify uncertainty in machine learning predictions.

Explore the basics and applications of two-layer neural networks.

Methods to enhance multi-label classification using partially labeled data.

Learn how contextual bandits adapt to changing rewards for better decision-making.

Explore the benefits of combining semi-supervised and active learning techniques.

New tools aim to enhance fairness in AI by providing access to diverse datasets.

New method improves differentiation between healthy and diseased patterns in medical images.

New methods enhance control of false discovery rate in binary data analysis.

New methods enhance tool recognition in surgical videos for better outcomes.

A new tool to adjust survey data for better population representation.

A new model enhances training data for semantic segmentation in AI applications.

A novel method combines machine learning with classical mechanics to analyze physical systems.

A new method enhances Gaussian mixtures for better predictions in complex distributions.

New methods improve training speed and accuracy of GNNs using quantization.

FGNNs improve learning of complex relationships in graph data.

Discover how PINNs integrate physics and data for solving equations.

This research explores the evolution of dynamic networks using motifs and node roles.

This article examines current popular topics in computer science research.

A new method improves custom word recognition in ASR systems for languages with limited data.

Explore how lattice Lipschitz operators help approximate complex functions.

A novel approach improves visibility in challenging low-light hazy scenes.

A new method streamlines data analysis by refining cluster and factor models.

This article highlights new methods to clarify spectral clustering results for text documents.

This article discusses self-checking methods for enhancing language model accuracy.

This study enhances the robustness of deep learning through dynamic model selection.

A new framework to ensure fairness in AI systems while in operation.

Enhancing VQA models by balancing visual and text features.

New evaluation metrics improve model assessment in unsupervised domain adaptation.

Introducing a neural network that efficiently approximates Wasserstein distance for complex point sets.

A new method enhances CLIP's image classification using contextual information.

Calibrating deep learning models ensures reliable predictions in critical applications.

This method improves model training amidst incorrect label challenges.

A new method addresses feedback biases in recommendation systems for two-sided interactions.

A data-driven approach to model Hamiltonian systems effectively.

A novel approach to analyze ADABOOST using truth tables for better classifier insights.

A new method improves model fairness by generating synthetic data samples.