Latest Articles for Neural Networks

A new method enhances image translation quality and control over styles.

This article presents a novel similarity measure for neural network representations using contrastive learning.

A new method enhances the training of Feedforward Neural Networks through smart heuristic selection.

Effective workload prediction using advanced GRU neural networks for resource management.

Using importance sampling can enhance neural network training efficiency and performance.

This article examines how loss functions affect machine learning model performance.

Enhanced 3D modeling using multi-view stereo and neural rendering.

Exploring the design and effectiveness of multi-task learning in computer vision.

Explore how invertible convolution enhances image processing capabilities.

A new framework enhances Vision Transformers by utilizing task labels for better performance.

This research improves MIMO detection using a hybrid approach with self-attention.

Research reveals time delays enhance reservoir computing efficiency and performance.

Using random matrix theory to enhance the performance of deep neural networks.

A new method for selecting valuable projections in CT scans boosts image quality.

SDM offers a way for networks to learn continuously without forgetting old information.

This article discusses the causes and implications of variance in neural network performance.

Exploring the role of small neural motifs in volatile memory storage.

This article explores how ensemble methods improve quantum neural networks' performance and efficiency.

IPNN offers a new way to classify and cluster data effectively.

Research highlights learned equivariance's role in improving image recognition models.

New metrics for better evaluation of disentangled representations in models.

A study on ElegansNet reveals insights into better learning models inspired by C. elegans.

Introducing Receptive-Field Attention to enhance CNN performance in image tasks.

Improving wave function optimization in quantum systems using neural networks.

HDNNs offer stable training and approximation capabilities for complex tasks.

A new approach enhances single-layer perceptrons for complex tasks.

Expanding monotone operator methods for practical applications in neural networks and data analysis.

Meta-LTH enhances few-shot learning efficiency by pruning unimportant neural network connections.

Lazy learning improves AI training by focusing on mistakes.

A new model for better handling of continuous latent variables in machine learning.

This study examines counting behavior in linear RNNs and their limitations.

New method enhances learning by prioritizing hard samples in mini-batch training.

Examining Capsule Networks' strengths against traditional CNNs in robust image processing.

Researchers reveal how size and data influence AI learning through quanta.

HyperINR speeds up implicit neural representations for clearer scientific visualizations.

New method shows promise in improving image quality for medical imaging.

Exploring the integration of machine learning in studying Active Galactic Nuclei light variations.

Examining how neurons synchronize and its effects on brain functions.

Exploring the connection between RNNs and electrical circuits for better understanding.

New RNN cell designs improve image classification performance significantly.