# [Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks Batu Helvacioglu ⋅ Ana Vasilcoiu ⋅ Thijs Stessen ⋅ Thies Kersten Graph Neural Networks have recently gained recognition for their performance on graph machine learning tasks. The increasing attention on these models’ trustworthiness and decision-making mechanisms has instilled interest in the exploration of explainability tech- niques, including the model proposed in "GNNInterpreter: A probabilistic

Machine learning techniques are designed to mathematically emulate the functions and structure of neurons and neural networks in the brain. However, biological neurons are very complex, which makes artificially replicating

It is widely understood that ReLU neural networks are piece-wise linear mappings. I have tried to give an ‘almost no choice’ argument leading to a layer-wise context mechanism: From Piecewise Linear Networks to Context

Explore cutting-edge architectures designed to make neural networks and large language models (LLMs) faster, lighter, and more efficient without compromising performance. From streamlined Transformers to pruned and quantized models, discover how these innovative designs are revolutionizing the deployment of AI in resource-constrained environments

Abstract page for arXiv paper 2506.11869v1: How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data

Optimize applications with tuned routines to realize the maximum performance for your SiFive RISC-V applications including neural networks, linear algebra, and signal processing

I have been meaning for a while to establish a setup to implement neural network based algorithms on smaller microcontrollers. After reviewing existing solutions, I felt there is no solution that I really felt comfortable with. One obvious issue is that often flexibility is traded for overhead. As always, for a really optimized solution you

Python, numpy, machine learning, neural networks, perceptrons

Nested Learning: The Illusion of Deep Learning Architectures - A comprehensive guide to the arXiv paper revealing how neural networks learn at multiple

The fear of artificial intelligence (AI) overtaking jobs has been a prevalent concern, especially in fields like programming.