Literal Labs co-founder Alex Yakovlev and CTO Leon Fedden join ipXchange host Elliott Lee-Hearn to discuss Tsetlin Machines.

Abstract page for arXiv paper 2411.17164: X-MeshGraphNet: Scalable Multi-Scale Graph Neural Networks for Physics Simulation

Sampling Strategies for Recurrent Neural Networks Recurrent Neural Networks are currently one of the most powerful Machine Learning models. They are the method behind many advances in speech

When designing the architecture of neural networks, we need to decide the number of nodes to include in the output layer and choose the appropriate activation function. For regression tasks, the linear activation function is the most suitable option. On the other hand, for classification tasks, we have a choice between softmax and sigmoid activation functions

Differentiable logic programming in Python for elegant encoding and extending of GNNs towards more complex deep relational models

3. Linear Neural Networks for Regression search Quick search code Show Source Table Of Contents - 1. Introduction - 2. Preliminaries - 2.1. Data Manipulation - 2.2. Data Preprocessing - 2.3. Linear Algebra - 2.4. Calculus - 2.5. Automatic Differentiation - 2.6. Probability and Statistics - 2.7. Documentation 3. Linear Neural Networks for Regression - 3.1. Linear Regression - 3.2. Object-Oriented Design for Implementation - 3.3. Synthetic Regression Data - 3.4. Linear Regression Implementation from

NeurIPS 2020 Evaluating Attribution for Graph Neural Networks Review 1 Summary and Contributions: Systematic evaluation of attribution methods for graph neural networks, incl. code/data for a benchmarking suite. Strengths: + Attribution for GNNs is still under studied and the combination of performance comparison for several GNN attribution methods with an accompanying open-source benchmarking suite for GNN attribution methods that includes realistic datasets and associated code are good contributions to th

Graph neural networks (GNNs) emerge as a powerful family of representation learning models on graphs. To derive node representations, they utilize a global model that recursively aggregates information from the neighboring nodes. However, different nodes reside at different parts of the graph in different local contexts, making their distributions vary across the graph. Ideally, how a node receives its neighborhood information should be a function of its local context, to diverge from the global GNN model s

Explore practical methods for mastering image classification using TensorFlow Hub. This article provides step-by-step guidance and insights into implementing ad...

Guide to Neural Network Machine Learning. Here we discuss the introduction to Neural Network Machine Learning with algorithms, & benefits