Latest Articles for Computational Models

Innovations in adaptive LIF neurons enhance performance in temporal and spatial tasks.

Examining how transformers learn from context without needing retraining.

New modeling techniques enhance our understanding of bacterial movement.

Research reveals unique symmetry restoration in many-body localized systems through the Mpemba effect.

Exploring how defects influence entanglement in quantum systems.

A new method using AutoSparse for efficient neural network pruning at the start.

New methods improve predictions of coastal dynamics using deep learning.

A look into Sparse Mamba, a method for better language model control.

A novel approach to enhance GFlowNet training with policy-dependent rewards.

This article presents a model to study error spread in quantum computing.

This article discusses the connection between RvNNs and Transformers through CRvNN and NDR.

A new method enhances language understanding in Transformer models using non-linear geometries.

Exploring how errors in catalytic computation can expand computational capabilities.

Explore the complexity of finite automata using translucent letters in language recognition.

HetSheaf improves data representation in heterogeneous graphs for better model performance.

An overview of string functions and their significance in computing.

Exploring the impact of recurrence on the effectiveness of Transformers in language tasks.

Utilizing reduced-order modeling for faster earthquake ground motion predictions.

New methods enhance GNNs for challenging graph types.

Examining how BERT interprets words with multiple meanings.

Researchers develop models that mimic brain processing using light and superconducting systems.

A new method enhances understanding and reliability of neural networks.

A new method improves chemical structure representation for enhanced analysis and efficiency.

Quantum systems offer a reliable method for producing unpredictable numbers.

This article discusses methods to better understand neural networks through Sparse Autoencoders and Mutual Feature Regularization.

Exploring the capabilities and challenges of Transformer technology in understanding language.

A fresh method to understand causal relationships in dynamic environments.

This research improves Knowledge Graphs using refined negative sampling techniques for better model performance.

Discover how NERDSS models particle interactions and reveals complex patterns in nature.

Exploring how different neurons enhance brain performance and influence machine learning.

Scientists uncover efficient pathways for molecule movement using advanced models.

Discover how neural networks enhance our grasp of the Hubbard model and quantum states.

A new method lets neurons work independently, enhancing neural network training.

A look at Mamba and State-Space Models in AI capabilities.

Learn how neural networks improve through training and data structure.

Graph-Generating State Space Models enhance how machines learn from complex data.

ReMoE brings flexibility and efficiency to language models with dynamic expert selection.

Dive into the complexities of how neural networks learn and interact.