Latest Articles for Neural Networks

This article explores the impact of Massive Activations in attention-based GNNs.

Exploring the impact of predictive coding on neural network learning methods.

A new method allows for easier conversion of ANNs to SNNs with less energy use.

Using neural networks to enhance variational Monte Carlo in quantum systems.

Exploring the role of non-autoregressive networks in solving complex optimization problems.

A novel approach to combine common and unique data from various observers.

This study evaluates neural networks for replicating spring reverb characteristics.

A study introduces a method to distinguish real images from generated ones using advanced techniques.

A new method improves neural networks by focusing on the Jacobian for structured outputs.

A new graph-based method enhances skeletonization for anatomical shape analysis.

New methods improve secure computations in neural networks while preserving privacy.

Insights into how brain neurons communicate and process information effectively.

New methods improve depth estimation using single images through enhanced data augmentation.

This research analyzes Mamba's performance in speech tasks, emphasizing sound reconstruction and recognition.

An evaluation of KANs for tasks in high-energy physics.

Introducing GSSC, a framework to improve graph learning without traditional message passing.

This article reviews dropout methods for boosting small language models' performance.

A look at effective sampling methods for wide Bayesian Neural Networks.

Exploring the potential of quantum neural networks in various fields.

Y-Drop improves dropout by focusing on neuron importance, enhancing model performance.

A new model improves the combination of neural networks and traditional algorithms.

A new method for simplifying high-dimensional data without prior knowledge.

This study highlights foundational models' effectiveness in improving medical image segmentation.

A look at training efficiency in CNNs and BCNNs using MNIST and CIFAR-10.

This research explores neural decoders to improve error correction in quantum computing.

Spiking neural networks improve communication systems by enhancing efficiency and performance.

Exploring Sparse Neural Networks and their performance with challenging training data.

A new framework improves learning from structured, noisy data using diffusion principles.

Introducing S-STE, a novel approach to improve sparse neural network training efficiency.

Discover how FastVPINNs improve fluid dynamics modeling using neural networks.

A novel method for controlling double pendulums shows significant improvements in stability and adaptability.

A new method ensures stability in neural network controllers for critical applications.

Explore adaptive sampling for better neural network performance with symmetric data.

Advancements in neural networks improve understanding of solar magnetic activity.

A new method improves feature transfer in implicit neural representations for images.

This study examines performance and conditions for quantized neural networks under fixed-point arithmetic.

FKAN improves image and 3D shape representation using learnable activation functions.

Discover how KANs offer an efficient alternative to traditional neural networks.

SparX enhances image processing by mimicking the human visual system.

A machine learning approach harnessing motion for effective visual data learning.