Latest Articles for Regularization

Fiedler regularization improves neural network performance by addressing overfitting effectively.

CoReFace improves open-set face recognition using innovative learning techniques.

This technique improves ultrasound images by using sound wave interactions.

A method for reconstructing shapes from noisy data using regularization techniques.

The adaptive Lasso improves data analysis by managing outliers and providing reliable estimates.

A new method for estimating Markov parameters in noisy environments for better system performance.

Exploring how multicalibration improves fairness in AI predictions.

This work focuses on improving 3D modeling through learnable coordinate transformations.

Discover how human teachers enhance growing-batch reinforcement learning methods.

Combining deep learning and traditional methods for image reconstruction.

Exploring quantum fields in curved spaces challenges traditional physics concepts.

A new method reduces spots and layers in proton therapy, enhancing treatment efficiency.

A new approach to improve attention computation in large language models.

Research sheds light on brain information processing through neural encoding models.

This article discusses a method to improve neural networks' learning of complex patterns.

This study examines random smoothing techniques to improve machine learning model performance.

Examining innovative methods in predictive control for dynamic systems.

This article explores the significance of sparse solutions in tensor complementarity problems.

ProFusion offers quick customization of images from text while preserving details.

Discover how DNNs enhance predictions for weakly dependent data.

Two deep learning models enhance shape optimization for aerodynamics, reducing manual efforts.

Discover how self-supervised learning enhances machine learning with unlabeled data.

Effective strategies to improve model performance on imbalanced datasets.

Examining methods to reduce the difference between two submodular functions in optimization.

A look at regularization methods improving stability in data analysis.

A look at how implicit regularization addresses infinities in particle decay calculations.

New approaches aim to improve GCN performance and generalization.

PlaNeRF enhances 3D modeling from 2D images, improving geometry and image quality.

A look at how spatial point processes help model events in various contexts.

Exploring the Deep Unconstrained Features Model and its impact on neural networks.

A new approach improves unpaired image translation using neural Schrödinger Bridges.

A new approach enhances accuracy in solving complex inverse problems.

A new method simplifies model predictions by analyzing training sample contributions.

This research explores how adversarial attacks affect Bayesian inference models.

A new method improves anomaly detection across various datasets using stable density estimation.

Improving signal recovery methods using advanced regularization techniques.

Understanding smooth functions can improve learning, but they require sufficient data.

Yoked Neural Networks improve information sharing and processing in neural systems.

A faster Bayesian approach improves spike detection in neuroscience research.

Exploring continual learning techniques applied to complex graph structures.