In this post, we discuss our research on predicting drug-disease relationships using graph-based ML methods.

Learn the mathmatics behind the core of Deep Learning!

Abstract page for arXiv paper 1903.01042: CodeNet: Training Large Scale Neural Networks in Presence of Soft-Errors

Search Log In Do Deep Neural Networks Have Brain-like Representations?: A Summary of Disagreements Joseph Emerson 18 Nov 2024 0:07 UTC 9 points 0 comments 26 min read LW link  Contents TL;DR Introduction What are representations? Representations in humans and AI How brain-like are representations in modern AI systems? Why it could matter for AI alignment Reasons to think ANNs have brain-like representations ANNs Match Human Behavior and Errors in Several Domains ANNs Predict Brain Responses ANNs and

This article is the first in a series in which I will attempt to explain neural networks as simply as I can. Artificial Neural Networks are currently the most ubiquitous and most complex tools in the field of Artificial Intelligence. In many cases, they can have millions of neurons and be taught to solve tasks

NeurIPS Proceedings Search Link Prediction Based on Graph Neural Networks Muhan Zhang, Yixin Chen Advances in Neural Information Processing Systems 31 (NeurIPS 2018) Abstract Link prediction is a key problem for network-structured data. Link prediction heuristics use some score functions, such as common neighbors and Katz index, to measure the likelihood of links. They have obtained wide practical uses due to their simplicity, interpretability, and for some of them, scalability. However, every heuristic has

Introduction to Neural Network Verification A book by Aws Albarghouthi Deep learning has transformed the way we think of software and what it can do. But deep neural networks are fragile and their behaviors are often surprising. In many settings, we need to provide formal guarantees on the safety, security, correctness, or robustness of neural networks. This book covers foundational ideas from formal verification and their adaptation to reasoning about neural networks and deep learning. 📖 Book in PDF an

Probabilistically rewired message-passing graph neural networks (PR-MPNNs) enhance expressive power and predictive performance by dynamically adjusting graph structures

Home AI Weirdness Book: You look like a thing About Janelle Subscribe Search Sign in Sign up AI Weirdness: the strange side of machine learning Tag: neural network Total 8 Posts Pokemon generated by neural network By Janelle Shane On July 22, 2016 - 1 min read I’ve been playing around with char-rnn, an open-source torch add-on for character-based neural networks by Andrej Karpathy [http://t.umblr.com/redirect?z=https%3A%2F%2Fgithub.com%2Fkarpathy%2Fchar-rnn&t

NeuralIntegrator: simulating spiking neural networks with discontinuities I work in the field of computational neuroscience and model spiking neural networks (SNNs) with different synaptic plasticity rules, i.e. rules o