Latest Articles for Data Science

A novel approach to reward over-optimization in language models using uncertainty estimation.

ECToNAS streamlines the process of finding optimal neural network architectures.

Introducing adversarial hypervolume to better assess deep learning model performance.

New training framework enhances language model learning through structured data.

A new method uses text to enhance machine learning across different visual domains.

Study reveals key properties of inhomogeneous random matrices and spectral outliers.

Addressing fairness in machine learning for better statistical outcomes.

One4All-ST offers a streamlined way to predict urban movement and needs.

A new method enhances GNNs by improving graph structure recognition.

Exploring graph homomorphism to enhance GNN generalization capabilities.

A new approach combines causal representation learning and foundation models for better understanding.

A closer look at sentiments and discussions during key cryptocurrency events.

DiNAS offers a new way to create high-performing neural networks quickly.

This article examines how neural networks improve predictions with small initial weights.

A new method improves accuracy in topic recommendations for software projects.

A look at how computing power and algorithms have transformed language models.

A new method enhances forecasting accuracy using large language models.

Learn how GKEDM improves Graph Convolutional Network performance.

This article discusses the impact of human labeling errors on SCL and a new solution.

New model improves interlinear glossing for endangered languages.

Examining how network width impacts knowledge retention during sequential learning tasks.

A new framework enhances code completion by utilizing repository context.

Introducing Nonparametric Transfer Learning for better adaptability in data shifts.

Learn how self-supervised learning enhances recommendation systems by addressing user preferences.

A new method for efficient data analysis amidst outliers.

A framework that merges Lagrangian and Eulerian data for improved system state estimates.

This work analyzes the performance of simplified transformers in forecasting tasks.

Examining how noise in pre-training data impacts model performance.

A new approach enhances student performance in model training.

Understanding how Graph Neural Networks model complex relationships in data.

A new dataset combines real and synthetic data for better monitoring of forests.

New dataset and framework improve object detection in SAR imaging.

Exploring methods to enhance multiclass classification through surrogate loss techniques.

Understanding the importance of participation brokers in machine learning projects.

A new dataset aids in predicting signal loss in urban communication networks.

Learn about Stochastic Localization in sampling and its key processes.

New methods in distributed optimization improve efficiency and privacy in data-driven decision-making.

Learn how gravitational waves are changing our view of the cosmos.

Examining how uniqueness aids in solving convex optimization problems.

Exploring two data-driven approaches to control systems.