Temporal Graph Neural Networks (TGNNs) aim to capture the evolving structure and timing of interactions in dynamic graphs. Although many models incorporate time through
Skip to content SudoAll About Understanding Neural Network Architecture Posted on January 5, 2025March 3, 2026 by David Saliba What is a Neural Network? A neural network is a computational model inspired by the biological structure of the human brain. At its core, it consists of interconnected nodes called neurons, organised into distinct layers that process information hierarchically. These artificial neurons receive inputs, apply mathematical transformations, and produce outputs that feed into subsequent
NeurIPS 2020 Graph Random Neural Networks for Semi-Supervised Learning on Graphs Meta Review All reviewers appreciate the idea of the paper, its simplicity and its good empirical performance
Exploring the effects of neural network weight initialization strategies
This explores whether knowledge encoded in a model's weights and activations is always causally wired to what it outputs — or whether some stored information sits inert, gets suppressed, or routes aro
Neural Network-based Language Model (NLM) From GM-RKB A Neural Network-based Language Model (NLM) is a language model that is neural text-to-text sequence model . Context: It can be produced by a Neural Language Modeling System (that can solve a neural LM training task ). It can range from (typically) being a Pretrained Neural Language Model (LM) to being an Untretrained Neural Language Model (LM) . It can range from being a Character-Level Neural Network-based LM to being a Word/Token-Level Neural Network
Cargèse, Corsica October 21-25, 2024 The Barcelona and CNRS teams recently met in Cargèse within the context of the “Maturation and Plasticity” workshop, and with the aim to advance discussions of ongoing works and progress in WP3 & WP4. The workshop, coorganized by Remi Monasson (CNRS), was also a unique opportunity to advertise the experimental…
The article discusses the comparison of machine learning models, including neural networks and random forests, using the Rattle package. It highlights the performance of different models on a dataset, with random forests showing better results than neural networks. The author also mentions the availability of advanced tools and the potential for further research and experimentation with various models
Bayesian neural networks (BNNs) are a principled approach to modeling predictive uncertainties in deep learning, which are important in safety-critical applications. Since exact Bayesian inference over the weights in a BNN is intractable, various approximate inference methods exist, among which sampling methods such as Hamiltonian Monte Carlo (HMC) are often considered the gold standard. While HMC provides high-quality samples, it lacks interpretable summary statistics because its sample mean and variance i
Making a deep convolutional neural network smaller and faster