Latest Articles for Neural Networks

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.

A new approach enhances generative modeling efficiency and flexibility.

A new approach to improve transformer training efficiency using information pathways.

A method combining symbolic reasoning and neural networks for better decision making.

New techniques enhance image matching in medical analysis and diagnostics.

A study on how scaling and complexity affect neural network performance.

This article explores how transformers memorize data through multi-head attention.

Yoked Neural Networks improve information sharing and processing in neural systems.

A new method enhances 3D modeling from sparse and noisy inputs using depth images.

This study explores training Ising machines for AI tasks using a novel method.

A new GNN architecture improves attention mechanisms for better performance in deep layers.

Exploring feedback alignment as an alternative to traditional backpropagation in neural networks.

Exploring why SGD excels in generalization compared to traditional methods.

A new method improves how models handle unexpected data.

A new method leverages symmetry in data for better learning outcomes.