Latest Articles for Neural Networks

Introducing a new method to improve model defenses against adversarial inputs.

Efficient models using fewer parameters for improved performance in deep learning.

A new method improves efficiency by converting PINNs to SNNs for scientific applications.

This article highlights concerns about the robustness of neural network controllers.

This work presents a new method for solving complex PDEs using neural networks.

Revolutionizing medical imaging through innovative implicit neural representations.

A look at discrete neural networks and their unique training methods.

A unified approach enhances music analysis by integrating multiple structural elements.

Explore how thermodynamics principles can enhance geometric deep learning models.

ICDARTS improves neural network design efficiency and stability in machine learning.

New methods cut memory use for large language models on mobile devices.

A new approach enhances understanding of decision-making in deep neural networks.

A new method enhances AI models' resistance to adversarial examples while maintaining accuracy.

This study presents a new model for memory encoding in neural networks.

Study reveals how neuron activity shapes responses to visual inputs.

A study on how architecture affects model calibration in neural networks.

Improving AI's ability to classify unseen categories through innovative methods.

A method to adjust DNN performance in real-time for efficiency.

A new approach enhances data shuffling for better model training.

New training methods improve feature extraction in autoencoders.

A new approach to reduce memory use in neural networks through 4-bit optimizers.

A new method for training GNNs efficiently one layer at a time.

Deformable attention improves efficiency and accuracy in visual recognition tasks.

New algorithms enhance memory retrieval in Hopfield networks for better performance.

New methods for training optical neural networks using light show promise for efficiency.

A new approach helps DRL systems adapt quickly to changing environments.

Learn how the learning coefficient quantifies degeneracy in deep neural networks.

A new method enhances Bayesian neural networks for quick and reliable predictions.

New neural networks designed for better performance in resource-limited devices.

Discover the mechanics and applications of Echo State Networks for time series data.

Neural ODEs merge neural networks with dynamic systems for advanced learning.

This study presents a new method to enhance learning in neural networks.

A novel approach simplifies neural network training and reduces overfitting.

A new approach for training neural networks inspired by brain function.

A new approach to improve neural networks using Partial Group Equivariant Non-Expansive Operators.

A novel approach to machine learning improves efficiency and speed in robotic tasks.

Introducing a benchmark to verify neural networks coded in plain C.

New Edge of Stability model enhances memory and performance in neural networks.

A new method improves self-supervised learning through ensemble learning for better predictions.

A study on how RNNs learn and adapt their object classifications.