Latest Articles for Regularization

iDARR offers a fresh approach to recover clarity from noisy images.

Examining adversarial training for stronger machine learning models against attacks.

Research shows general regularization methods boost off-policy RL agent performance across tasks.

This article discusses sampling techniques from mean-field models in complex systems.

A fresh approach for assessing neural networks without extensive training.

A new method improves the estimation of memory kernels in particle dynamics.

A new method enhances privacy in federated learning against gradient inversion attacks.

A look into the gravitational interactions and motions of the Earth-Moon system.

This article discusses methods to improve deep learning's resilience to adversarial examples.

Study reveals factors affecting single-layer GCN's performance on various data models.

This article discusses a new method for enhancing Extreme Learning Machines in various applications.

Examining methods for better signal detection through integer solutions.

A new method enhances accuracy in locating moving sound sources using microphone arrays.

New techniques in photoacoustic tomography improve medical imaging and diagnosis.

A look at how cosmologists manage infinite values in their calculations.

Discover a new method for image restoration using self-guided reconstruction.

A new method enhances the extraction of insights from aviation accident reports.

This article discusses methods for training two-layer ReLU neural networks efficiently.

Exploring robust optimization methods for uncertain data in decision-making.

SHIELD Regularization enhances AI performance and explainability for better understanding of decisions.

Study investigates how near-interpolating models perform on unseen data.

Research highlights the importance of timing over specific speaker features in diarization models.

Explore the connection between thermodynamics and machine learning to simplify complex systems.

A new method for tackling nonsmooth optimization problems with constraints.

A look at how adversarial learning improves signal separation techniques.

The Projected Newton method improves efficiency in Bayesian linear inverse problems.

A new method enhances autoencoders for better data representation.

Soft Preference Optimization improves how language models align with human preferences.

Learn how to build and refine models of dynamic systems using advanced techniques.

Study of reconstructing unknown terms in PDEs from noisy data using regularization methods.

A new method improves modeling for active metamaterials with pneumatic actuation.

Enhancing State Space Models through better training techniques for time series data.

New methods improve matrix completion accuracy and efficiency across various fields.

A method to reconstruct shapes of obstacles concealed in fluids through boundary measurements.

A look at how dependence measures help analyze relationships between multiple variables.

Explore the principles and applications of Kernel Ridge Regression in various fields.

An overview of the Casimir effect and its implications in quantum physics.

This article compares LoRA and full finetuning on performance and memory use.

Introducing a new method for efficient training of structured neural networks.

Research on black holes may change our view of gravity and spacetime.