Latest Articles for Neural Networks

A look at bi-CryptoNets and their impact on data privacy.

Introducing a method to reduce forgetting in neural networks while learning new tasks.

New neural networks enhance control over outputs and improve performance in various tasks.

A new energy-efficient approach to associative memory using a virtual oscillator network.

This work proposes a robust approach for deep reinforcement learning against input data attacks.

A look at rule extraction methods for improving RNN interpretability.

Research focuses on improving training efficiency in dynamic graphs.

A new algorithm enhances quantum learning with imperfect data.

A look into Cross-Task Linearity and its effects on model performance.

T-HGIN enhances hypergraph processing with efficient multi-step learning.

This research examines vulnerabilities in SNNs combined with federated learning techniques.

CAST improves the efficiency of self-attention in Transformer models for long sequences.

Denoising models enhance data transmission quality in challenging environments.

New method improves wireless signal quality using user power measurements.

Exploring efficient methods for learning RBMs using quantum algorithms.

A new approach to deep learning that improves efficiency and stability.

Learn about RBMs and their applications in data analysis.

This article examines how neural networks manage resources for complex tasks.

This study improves GNN performance through effective graph explanations.

StableMask enhances attention distribution for better language model performance.

This article discusses how architectural choices shape the learning process of DNNs.

Architectural backdoors pose serious security risks in neural networks, often remaining undetected.

New artificial neurons based on Bose-Einstein condensate show promise for advanced AI applications.

New strategies allow Trojan attacks to bypass detection methods effectively.

Intra-Fusion combines neurons, making neural networks smaller and more efficient.

This study explores neural networks for score function estimation in diffusion models.

A novel method improves machine learning model security and efficiency.

A look into how NN representations mimic human memory processing.

A novel approach improves solutions to Fisher's equation with neural networks.

This article discusses continual learning challenges and potential solutions in machine learning.

This study enhances fine-tuning efficiency in neural networks using transductive active learning.

New methods improve estimation in complex scientific models.

An overview of transformers and their impact on data processing.

A new method improves event classification in particle physics using machine learning.

Exploring effective methods for visualizing complex shapes in neural networks.

This article discusses how traveling waves can change AI memory systems.

An in-depth look at why transformers struggle with sensitive functions.

New memory models improve efficiency in reinforcement learning training processes.

Learn how new pruning methods enhance efficiency in deep neural networks without sacrificing accuracy.

A look into how scaling laws shape neural network efficiency and accuracy.