Open Menu Proceedings of the AAAI Conference on Artificial Intelligence Search - Home - / - Archives - / - Vol. 39 No. 15: AAAI-25 Technical Tracks 15 - / - AAAI Technical Track on Machine Learning I # Neural Reasoning Networks: Efficient Interpretable Neural Networks with Automatic Textual Explanations ## Authors - Stephen Carrow - International Business Machines - Kyle Erwin - International Business Machines - Olga Vilenskaia - International Business Machines - Parikshit Ram - International Business
# A Gentle Introduction to Graph Neural Networks This article is one of two Distill publications about graph neural networks. Take a look at Understanding Convolutions on Graphs to understand how convolutions over images generalize naturally to convolutions over graphs. Graphs are all around us; real world objects are often defined in terms of their connections to other things. A set of objects, and the connections between them, are naturally expressed as a graph. Researchers have developed neural network
Press "Enter" to skip to content Curated SQL A Fine Slice Of SQL Server open menu Search About The Problem of Reproducability in Neural Networks Published 2022-12-01 by Kevin Feasel Pete Warden explains a problem : Last week I had a question from a colleague about reproducibility in TensorFlow, specifically in the 1.14 era. He wanted to be able to run the same training code multiple times and get exactly the same results, which on the surface doesn’t seem like an unreasonable expectation. Machine learning
Abstract page for arXiv paper 1502.03240: Conditional Random Fields as Recurrent Neural Networks
Neural networks (NNs) have become a ubiquitous machine learning technique in a wide range of domains. In 2003, some researchers surveyed the literature on verification of NNs and concluded that they “represent a class of systems that do not fit into the current paradigms of software development and certification”. Wondering if that was still the case after the NN renaissance that’s taken place over the last ten years or so, I began looking for recent work on verifying interesting properties of neural
This explores whether networks carve a hard task into separable pieces on their own — and whether that modularity is something they grow naturally, something you have to force, or something that's qui
How does a neural network actually learn to be less wrong? Not the hand-wavy version. The real one.... Tagged with ai, machinelearning, datascience, deeplearning
Understanding the Dropout Layer: Improving Neural Network Training and Reducing Overfitting with Dropout Regularization
Bio-inspired Spiking Neural Networks (SNN) are now demonstrating comparable accuracy to intricate convolutional neural networks (CNN), all while delivering remarkable energy and latency efficiency when deployed on neuromorphic hardware. In particular, ANN-to-SNN conversion has recently gained significant traction in developing deep SNNs with close to state-of-the-art (SOTA) test accuracy on complex image recognition tasks. However, advanced ANN-to-SNN conversion approaches demonstrate that for lossless conv
Build a precise mental model of neural networks from affine layers and nonlinearities to backpropagation, losses, initialization, optimization, regularization, CNNs, RNNs, and Transformers. Includes a shape-safe PyTorch example, evaluation and reproducibility practices, and the limits of biological analogies and benchmark claims