Latest Articles for Computational Models

Graph Mamba Networks offer a new approach for efficient graph analysis.

An overview of unpredictable behavior in interacting quantum systems.

Study of how agents reach agreement with limited communication.

This study evaluates models for tracking shifts in word meanings across languages.

Examining how information changes and spreads in quantum spin glasses using key models.

Introducing a method to speed up language models while improving resource efficiency.

FUTE offers a new approach to task embeddings across multiple models.

A method to connect neural networks with symbolic reasoning for better performance.

A new framework enhances graph pre-training by selecting and weighing tasks effectively.

Discover how Step CRNs simulate complex circuits using simple rules.

A look into the behavior of interacting quantum systems and their applications.

A new method improves the generation of drug-like molecules with optical isomerism.

Molecular shapes impact drug design and interactions.

Exploring the intersection of category theory and deep learning frameworks.

A new method enhances the learning capabilities of recurrent neural networks.

A look into the various types of cortical interneurons and their roles.

An overview of finitely presented Heyting pretoposes and their significance in logic.

Examining the complexities of PCTL satisfiability in computer science.

Research enhances decision-making efficiency and accuracy in spiking neural networks.

New strategies improve reasoning in complex argument frameworks for AI applications.

A new model enhances simulations in fluid dynamics, benefiting multiple industries.

This study investigates pseudoentanglement and its dynamics using automaton circuits.

This study reveals how sCRNs mimic other computational models.

A look into AAqvist's logics and small model constructions.

Study reveals magnetic braking's impact on gas in early star formation.

A look at compositionality and generalization in machine learning language models.

Study highlights the significance of temporal parameters in neural network performance.

A new architecture improves machine speech recognition using spiking neural networks.

BiSHop enhances deep learning for tabular data with innovative techniques.

A Neural Network emulator enhances the study of galaxy clustering data.

Introducing a new method for efficient training of structured neural networks.

An overview of the Relational Machine Calculus and its key features.

A new model improves graph generation with discrete-state and continuous-time methods.

A study on the strengths and weaknesses of two graph processing models.

AdpQ offers a new way to enhance LLM efficiency without extra data.

Research reveals how synthetic ferrimagnets can influence electronics through new magnetic properties.

A novel model enhances insights into how neurons function and interact over time.

State space models offer efficient processing in natural language tasks, challenging traditional transformers.

Exploring how transformers tackle various graph reasoning tasks effectively.

A new model streamlines AI image and video creation with improved speed and quality.