Latest Articles for Neural Networks

This article discusses the use of Physics-Informed Neural Networks in solving quantum mechanics problems.

Exploring how neural networks recognize symmetries in data through equivariance.

A new method predicts neural network performance using only weight parameters.

This article investigates the necessity of the query component in transformer models.

New analog systems use light for faster, energy-efficient information processing.

Learn how group-invariant GANs improve data efficiency in generative models.

New methods improve understanding of neuronal connections despite incomplete data.

Caterpillar is a novel MLP architecture for capturing local image details.

A look into weighted classification metrics and score-oriented losses in neural networks.

Exploring the Deep Unconstrained Features Model and its impact on neural networks.

SHARP addresses catastrophic forgetting in deep neural networks through innovative learning techniques.

New neural networks learn transformations directly from data, improving efficiency and understanding of symmetries.

A novel method for training neural networks that combines classification and reconstruction.

New approach improves how AI recognizes unique combinations of attributes and objects.

A look at RTRL's potential and obstacles in machine learning.

Study reveals how deep networks excel despite noise in training data.

A look at how benign overfitting can benefit machine learning models.

A review of smaller Vision Transformers suitable for mobile applications.

Examining the effectiveness and challenges of unlearnable datasets in protecting private information.

A look into the mechanics and applications of spiking neural networks.

Weight normalization improves neural network training and performance, even with larger weights.

Aligned-MTL addresses challenges in multi-task learning for better performance.

A study on how CoT improves learning in multilayer perceptrons.

A novel approach to improve neural network training through quantized optimization.

Examining how transformers learn to understand language hierarchies through extended training.

This study introduces innovative metrics to evaluate RNNs and transformers without training.

Exploring the effectiveness of evolutionary strategies in finding sparse network initializations.

A new method leveraging graphs to identify adversarial attacks on neural networks.

A new method enhances how neural networks explain their decisions.

A new method enhances generalization of sequence models across varying lengths.

BT-Cell enhances recursive neural networks for improved language understanding.

This article examines how deep networks function through the extractor and tunnel.

Exploring the potential and challenges of spiking neural networks in computing.

LLMatic combines large language models and quality-diversity strategies for efficient neural architecture search.

Examining how gradient descent favors simpler solutions in deep learning models.

A new system improves image quality by merging event camera data with blurry images.

Exploring various generative models and their unifying framework.

Cone attention improves data relationships in models with hierarchical structures.

Examining OODF and its impact on continual learning in artificial intelligence.

Examining the role of frequency and compositionality in subword tokenization methods.