Latest Articles for Computational Models

New method enhances learning in Spiking Neural Networks by incorporating delay adjustments.

The Particle Dual Attention Transformer enhances jet tagging techniques in particle physics.

Programmable matter allows materials to change shape and properties, offering diverse applications.

Evaluating the efficiency of indefinite causal order in quantum computing.

A new model promises better learning for artificial intelligence through brain-inspired methods.

Exploring the potential of qudits in quantum computing and their advantages.

Research combines machine learning and quantum physics to identify quantum correlations in qubits.

Understanding how neutrons interact within atomic nuclei shapes nuclear physics.

UGT combines local and global graph information for better data analysis.

A look into how jets inform our understanding of particle interactions.

ReLiCA algorithm simplifies model creation using Cellular Automata rules and effective settings.

A new method combines language-based rewards with traditional approaches in RL.

A model-free method for decision-making in uncertain environments using Bayesian techniques.

A look at best-effort synthesis for agents facing uncertainty.

Improving Knowledge Graph Completion with advanced rule aggregation techniques.

An overview of Freezing 1-Tag Systems and their computational significance.

A new approach to simplify simulations of processes with varying speeds.

New Edge of Stability model enhances memory and performance in neural networks.

This study focuses on improving multilingual models for low-resource languages through vocabulary adaptation.

New models improve how machines remember and generalize data.

This paper connects parallel pushdown automata with commutative context-free grammars.

An overview of constituency parsing and M-monoid parsing's role in NLP.

A look into neuron networks and their impact on brain functionality.

A new approach boosts performance in data representation and transformation tasks.

A new protocol ensures privacy in computations using SN P systems.

Combining models to boost accuracy and efficiency in deep learning.

A novel approach merges state space models with spiking neural networks for better data handling.

Introducing SAR-GNN: A new method for effective graph classification.

A look at neural network learning frameworks and their implications for AI development.

Research highlights the impact of smoothness on adversarial attacks in image generation.

Investigating the role of multifractality in quantum particle behavior and applications.

A new framework improves accuracy in node classification with limited labeled data.

A new model that improves graph learning by focusing on node pairs.

This article explores methods to study mixed crystals and their applications.

Examining weak correlations and linear behavior in neural networks during training.

A new method using octagons to improve knowledge graph representation.

A look into the essential theory behind deep learning models.

An overview of how simple rules create complex behaviors in triangular automata.

Examining how GFlowNets generalize to untested areas and their application potential.

Utilizing quantum mechanics for truly random number generation.