Latest Articles for Neural Networks

A new model enhances predictions by revisiting previous guesses.

This study examines the effectiveness of Sparse Autoencoders in understanding language model features.

A new approach to secure short message transmission using deep learning techniques.

Exploring the effectiveness and questions surrounding recurrent neural networks in sequential data processing.

HEN improves memory retrieval in neural networks by enhancing pattern separability.

Learn how hyperparameters affect neural network performance and complexity.

Combining graph neural networks and variational autoencoders enhances image classification accuracy.

A new method enhances SNN performance while saving energy through weight compression.

A new method enhances the grouping of neural networks for better understanding.

SGDrop helps CNNs learn better from limited data by broadening their focus.

Exploring how data structure impacts machine learning performance.

Examining plasticity loss in continual learning and the role of sharpness.

New methods optimize large language model quantization, enhancing efficiency and accuracy.

Exploring invariant and equivariant maps to enhance neural networks.

Dynamic learning rates and super level sets enhance stability in neural network training.

Introducing a new method to improve deep learning models by reducing overfitting.

Using implicit neural networks to enhance speed of sound measurement in tissues.

A look at the Codec-SUPERB challenge results and codec performance metrics.

A novel approach to address memory issues in machine learning.

Introducing a neural model that improves graph similarity measurements by considering edit costs.

This study analyzes how well Transformers can memorize data in various contexts.

Examining how SSL models memorize data points and its implications.

A new method enhances model efficiency while reducing size.

A new framework improves neural networks for devices with limited resources.

Cottention offers a memory-efficient alternative to traditional attention methods in machine learning.

A framework merging different knowledge types to improve model performance.

This article examines MLPs and KANs in low-data environments.

A look into how CNNs learn image features and their universal similarities.

Analyzing over-parameterization in RMLR and future research directions.

A study comparing privacy threats in spiking and artificial neural networks.

MAST improves efficiency in training multiple AI agents through sparse methods.

A new framework improves learning efficiency in online continual learning.

Zorro functions provide smooth solutions for enhanced neural network performance.

SATA improves the robustness and efficiency of Vision Transformers for image classification tasks.

Introducing counter-current learning as a natural alternative to traditional training methods.

Analyzing the effects of pruning methods on GoogLeNet's performance and interpretability.

A new method enhances chaotic behavior learning using reservoir computing.

This article discusses neural networks that effectively blend approximation and generalization.

Exploring new methods for reducing text data size efficiently.

A new approach to neural networks using symmetry and structured matrices.