As post hoc explanations are increasingly used to understand the behavior of graph neural networks (GNNs), it becomes crucial to evaluate the quality and reliability of GNN explanations. However, assessing the quality of GNN explanations is challenging as existing graph datasets have no or unreliable ground-truth explanations for a given task. Here, we introduce a synthetic graph data generator, ShapeGGen, which can generate a variety of benchmark datasets (e.g., varying graph sizes, degree distributions, h
Having recovered somewhat from the last push on deep learning papers, it's time this week to tackle the next batch of papers from the 'top 100 awesome deep learning papers.' Recall that the plan is to cover multiple papers per day, in a little less depth than usual per paper, to give you a broad…
Neural networks are inspired by the human brain, consisting of interconnected nodes called neurons that mimic biological neural connections. Each node
NeurIPS Proceedings Search Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization Hyeonwoo Noh, Tackgeun You, Jonghwan Mun, Bohyung Han Advances in Neural Information Processing Systems 30 (NIPS 2017) Abstract Overfitting is one of the most critical challenges in deep neural networks, and there are various types of regularization methods to improve generalization performance. Injecting noises to hidden units during training, e.g., dropout, is known as a successful regularizer, but i
Abstract page for arXiv paper 1609.01596: Direct Feedback Alignment Provides Learning in Deep Neural Networks
Menu How can we help you? Search For Search Neural Network – Output Discussion Created September 30, 2016 Author Ward Systems Group Support Category Neural Networks Once you’re familiar with neural networks, you realize they can help you solve many different problems. You also know from experience there is more than one way to approach a problem. For example, if you’re creating a neural network to predict stocks, you can predict a number of things: stock price, change in a stock price, percent change
Random Forests and Neural Network are the two widely used machine learning algorithms. What is the difference between the two approaches? When should one use Neural Network or Random Forest
A first example of a neural network · Tensors and tensor operations · How neural networks learn via backpropagation and gradient descent
NeurIPS Proceedings Search Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks Aaron Voelker, Ivana Kajić, Chris Eliasmith Advances in Neural Information Processing Systems 32 (NeurIPS 2019) Abstract We propose a novel memory cell for recurrent neural networks that dynamically maintains information across long windows of time using relatively few resources. The Legendre Memory Unit~(LMU) is mathematically derived to orthogonalize its continuous-time history -- doing so by
Explore the intricate neural networks involved in decision-making, from the prefrontal cortex to the limbic system, and their impact on human behavior