Latest Articles for Neural Networks

PointTree offers an innovative solution for accurately reconstructing neuron connections in the brain.

Exploring the latest developments in models for processing long sequences of data.

This study examines how task similarity affects continual learning in neural networks.

This study examines how model size affects performance in Online Continual Learning.

This research explores how to manage neural activities with advanced control methods.

Exploring how deep learning models mimic human contour integration skills.

A method for compressing images while ensuring their security during transmission.

A look at methods to clarify AI decisions through gradient smoothing.

A new approach to neural architecture search offers flexibility and improved performance.

A new method improves medical image analysis while preserving critical details.

ESGNN improves scene graph generation from 3D point clouds by preserving symmetry.

Exploring how PANN models simulate hyperelastic beam behavior under stress.

A new approach improves learning efficiency in reinforcement learning environments.

New methods reveal insights into deep learning capabilities using tropical geometry.

Weight clipping enhances model performance in deep learning and reinforcement learning.

A new method enhances LoRA's efficiency and effectiveness in machine learning.

A new standard improves neural network speed and energy efficiency in computing.

A new model combining LSTMs with quantum computing for enhanced data handling.

KANs enhance image analysis and classification while using fewer resources.

Swish-T enhances model performance in deep learning through innovative activation techniques.

Investigating how overparameterized models outperform underparameterized ones in learning features.

An analysis of RNN-TPPs and their impact on event prediction accuracy.

Mutual learning boosts performance and uncertainty estimation in Bayesian Neural Networks.

Investigating how neural networks learn features during training.

Understanding how galaxies rotate reveals key insights into their structure and mass.

A new model enhances the simulation of string instruments for realistic sound.

A new method enhances self-supervised learning by adding a memory component.

A new convolutional layer design reduces parameters and improves interpretability in AI models.

This article explores solutions to oversmoothing in graph neural networks, focusing on GCNs.

Explore how batch size affects the training of machine learning models.

SineKAN offers improved speed and performance using sine functions in neural networks.

New research reveals complex patterns in machine learning training dynamics.

A study on energy behavior in deep learning networks enhancing signal analysis.

Examining how boundary conditions affect quantum reservoir computers' performance.

This paper examines factors influencing neural networks' ability to generalize from data.

Exploring new methods to improve uncertainty estimation in Bayesian neural networks.

ISQuant offers a new approach to quantization for efficient model deployment.

Discover how adaptive dynamic quantization enhances VQ-VAE models for better data representation.

This study examines how neural networks interpret speech using spectrograms.

This study explores how transformers learn from Markov processes through initialization and gradient flow.