Latest Articles for Neural Networks

SpikExplorer simplifies the design of energy-efficient Spiking Neural Networks for portable devices.

Study investigates how near-interpolating models perform on unseen data.

Exploring how transformers favor low sensitivity functions for improved performance.

A new perspective on how neural networks learn features through expert-like paths.

A method to improve machine learning models' knowledge retention during new task training.

Structurally Flexible Neural Networks improve adaptability for diverse tasks.

A new approach to reduce CNN complexity while maintaining performance.

This study examines how small weight initializations impact neural network training.

Research focuses on improving neural network verification with minimal NAP specifications.

A look into how parameter adjustments shape neural network training.

This research reveals task vectors that enhance visual model performance without extra examples.

New AI method improves cancer cell classification while addressing batch effects.

Analyzing the relationship between contrastive learning and traditional methods like PCA.

This research identifies promising new optimizers for deep learning models.

Explore how signal movement affects transformer performance and training.

New algorithms improve privacy and optimization in machine learning models.

Analyzing threats and defenses in federated learning against malicious attacks.

A new approach enhances training efficiency for RNNs using advanced optimization methods.

Exploring how neural networks can approximate functionals in data analysis.

Enhancing confidence intervals for DeepONet using split conformal prediction methods.

Examining how quantization can improve neural network performance and generalization.

Examining scaling strategies to enhance GNN performance in molecular graph tasks.

New algorithms boost efficiency in active learning with neural networks.

EncodeNet enhances DNN accuracy without increasing model size.

A look into how different neural networks learn from images.

A look into how neural networks process information and their implications.

GRAF enhances performance predictions for neural networks, boosting efficiency and interpretability.

This article discusses how transformers learn language structure through training methods.

New model improves depth estimation using event camera data through efficient algorithms.

DelGrad enhances learning in Spiking Neural Networks by focusing on spike timing.

SGD-PH combines first-order and second-order methods for better model training performance.

A method to improve image classification by minimizing biases in datasets.

Exploring how NCDEs reshape data learning and prediction.

New approaches improve understanding and transferability in neural networks.

Explore the rise and efficiency of Vision Transformers in image processing.

Study highlights the significance of temporal parameters in neural network performance.

Learn how PINNs combine machine learning and physics to solve complex problems.

Integrating multiple data types improves learning and retention in deep neural networks.

A fresh perspective on the inner workings of neural networks.

Discover a method to reduce neural network size without sacrificing performance.