Latest Articles for Neural Networks

A new model simplifies training and improves neural network performance across various tasks.

Explore how neuron networks synchronize their activities despite inherent challenges.

R-softmax improves model clarity by allowing zero probabilities for certain categories.

Examining how kernel interpolation performs in noisy data scenarios.

This study uses gaze data to enhance how computers find objects in images.

Exploring two methods to train MLPs with symmetric data.

Research shows structured pruning enhances performance of multi-task deep learning models.

A new method boosts gesture classification, handling both known and untrained gestures effectively.

Improving Spiking Neural Networks through innovative knowledge sharing techniques.

A look into NAS benchmarks and their impact on neural network design.

Research details how brain injuries affect neuron communication and recovery.

This study examines the role of retinal waves in neural connectivity.

A new method for optimizing machine learning while maintaining orthogonality efficiently.

A look at advancements in speech recognition models for efficiency and accuracy.

Exploring over-parameterization and activation functions in neural networks and their impact on performance.

New method improves deep learning models without complex tuning.

This article discusses a new method for stabilizing hypernetwork training.

A new benchmark to test ML systems on regular language classification.

A novel approach to improve object reconstruction behind reflective surfaces.

ATHEENA simplifies Early-Exit networks implementation on FPGA, improving performance and efficiency.

A novel layer for better image processing flexibility and user control.

Examining how Lambda Calculus aids neural networks in computational tasks.

B-CNNs improve image recognition through rotation and reflection capabilities.

A look into training methods for retrieval models using weak supervision.

This article discusses new methods to improve bilevel optimization techniques.

LipsFormer aims to stabilize training for Transformers, improving performance and reducing instability.

Researchers develop method to clarify neural network feature representation.

This article discusses methods for saving energy in AI applications on small devices.

This article discusses filter pruning methods to enhance neural network efficiency.

Learn how transformers process data and their impact on various tasks.

Introducing a method to utilize unlabelled data in Bayesian Neural Networks for better predictions.

A new algorithm enhances data augmentation by using label information for improved model training.

AOT-SNNs improve uncertainty estimation in spiking neural networks for better predictions.

GPT-4 shows promise in enhancing neural architecture search efficiency and effectiveness.

Examining individual fairness in Bayesian models compared to traditional neural networks.

Fiedler regularization improves neural network performance by addressing overfitting effectively.

ReRAM shows promise in improving neural network performance and efficiency.

New methods enhance our understanding of neural connections and brain functionality.

asRNN improves memory and training stability for long sequences in machine learning.

Exploring the Absolute activation function for improved classification performance.