Latest Articles for Neural Networks

LayerShuffle enhances the robustness of neural networks by enabling flexible layer execution.

Exploring how Hopfield networks mimic brain memory storage and retrieval.

Introducing a new method for Bayesian neural networks that improves uncertainty modeling.

Exploring fKANs and their impact on machine learning performance.

Study on the influence of receptive field size in U-Net models for image segmentation.

ElasticAST allows processing of variable length audio efficiently without losing important details.

A novel method simplifies complex 3D shapes with effective sweep surfaces.

This article investigates how neural networks process data through their representations.

A new approach enhances CNN training timing and efficiency.

Introducing a method that enhances learning from limited data without forgetting past knowledge.

A look into enhancing FPGA use in DNN applications with new techniques.

Introducing Group-and-Shuffle matrices for efficient fine-tuning of neural models.

A study on enhancing decision-making in limited-information chess through neural networks.

A new method using circular vectors improves efficiency in multi-label tasks.

LeRF combines deep learning and interpolation for better image resizing.

This article examines how Transformers reason and the role of scratchpads.

A novel method addresses key challenges in reinforcement learning through improved optimization techniques.

Examining the impact of periodic activation functions on learning efficiency and generalization.

CCL ensures neural networks maintain accuracy while learning new tasks.

Machine learning enhances quantum control techniques for improved technology applications.

Using neural networks to identify chiral magnetic waves in particle physics.

MambaVision combines Mamba and Transformers for better image recognition.

Study reveals how sparsity in AI models changes across layers during training.

DisMAE enhances model generalization across domains using unlabeled data.

A fresh approach to improve gamma-ray observations using neural networks.

A hybrid model improves image restoration using Spiking and Convolutional Neural Networks.

This article discusses new methods improving deep learning performance using nonlocal derivatives.

This article examines Adagrad's efficacy and its advantages over standard methods in large batch training.

A study on using neural networks for simulating material phase dynamics.

A study on machine learning techniques for modeling atomic systems.

A new method improves privacy while training deep learning models.

New methods reduce memory usage while maintaining performance in LLMs.

Learn how weight block sparsity boosts AI performance and efficiency.

This article examines how layer changes impact transformer model performance.

Learn how mini-batch SGD improves neural network training and feature selection.

New neural networks using memristors enhance efficiency and adaptability.

Examining dynamic methods for optimizing machine learning model training.

QCNNs offer new ways to identify phases of matter in quantum systems.

This article discusses methods to enhance neural networks through equivariance and symmetry.

A new system improves recommendation predictions using standard hardware efficiently.