Latest Articles for Neural Networks

Exploring improved learning rates in neural networks for scientific computing.

A new benchmark evaluates methods for measuring representational similarity in machine learning.

A new method enhances neural networks' ability to learn and apply knowledge.

This article examines advances in AI beyond transformers.

New methods for better control of RNNs enhance audio effect simulations.

Exploring how diffusion models transform and generate data through systematic noising and de-noising.

New methods improve training and performance of Physics-Informed Kolmogorov-Arnold Networks.

A new method uses graph neural networks to improve graph coloring efficiency.

A new method improves neural network training using a hybrid optimization approach.

A new approach to analyzing how image models withstand input changes.

A look into the bi-encoder architecture and its new perspective for improved search accuracy.

Exploring the role of geometric insights in improving representation learning methods.

A look at neural networks using detailed and simplified models.

A look into nonexpansive maps, fixed points, and their applications in mathematics.

NFE offers an efficient way to enhance model performance without heavy computational costs.

New methods boost translations for unseen languages without extensive data.

This study examines how certain neurons maintain stability in the visual system amidst changes.

New synapse model enhances energy efficiency in spiking neural networks.

Research introduces a new method for improving event camera image quality.

A detailed look at training and dynamics in machine learning.

Introducing an efficient way to synthesize realistic long-range axons in the brain.

A new method improves parameter selection for complex-valued neural networks in digital communication.

A look into the role of transformers in processing language.

New methods improve deep learning models' resistance to adversarial changes.

Exploring KANs as a new approach for efficient reinforcement learning models.

A look at deep learning models and their geometric properties.

Learn how pruning helps reduce neural network size while maintaining performance.

A new method using neural networks improves modeling of complex electromagnetic fields.

Exploring new methods for improved neural network performance in communication systems.

A new method improves speed and quality in generative diffusion models.

Exploring complex-valued neural networks in enhancing MIMO-OFDM communication.

A method to forecast FPGA requirements for neural networks before building them.

A novel algorithm enhances training efficiency for Random Fourier Neural Networks.

This study reveals the benefits of asynchronous processing in spiking neural networks.

Study reveals how memorisation evolves across layers in language classification tasks.

Using Darwinian concepts to improve neural network training methods.

A look at how superposition enhances neural network computation and efficiency.

Exploring how models move from memorization to effective generalization.

A novel approach simplifies parameter estimation in models lacking clear likelihood functions.

Improving optimization techniques for real-valued functions using a modified Mixed Newton Method.