Latest Articles for Neural Networks

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.

This study improves transfer learning by optimizing learning rates for each layer.

This study explores the role of feed-forward layers in code language models.

Combining sound and images for smarter recognition systems.

Exploring how neural networks use their learned weights effectively.

This article outlines a new approach using Test-Time Training for enhancing RNN performance.

A method to enhance model efficiency in machine learning through effective pruning strategies.

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.