Neural networks have been powering breakthroughs in artificial intelligence, including the large language models that are now being used in a wide range of applications, from finance, to human resources to healthcare. But these networks remain a black box whose inner workings engineers and scientists struggle to understand. Now, a team has given neural networks the equivalent of an X-ray to uncover how they actually learn
kevin frans blog Generative Adversarial Networks Explained tutorials Generative Adversarial Networks Explained Kevin Frans Read more posts by this author. Kevin Frans 28 Jun 2016 • 5 min read There's been a lot of advances in image classification, mostly thanks to the convolutional neural network. It turns out, these same networks can be turned around and applied to image generation as well. If we've got a bunch of images, how can we generate more like them? A recent method, Generative Adversarial
Abstract page for arXiv paper 1908.02620: Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks
In this post, we will see how to implement the feedforward neural network from scratch in python. This is a follow up to my previous post on the <a href="https://hackernoon.com/deep-learning-feedforward-neural-networks-explained-c34ae3f084f1">feedforward neural networks</a
Neural Networks Research FAQs on Neural Networks Updated version of FAQ is available in ftp://ftp.sas.com/pub/neural/FAQ.html A somewhat old version of FAQ is available in http://wwwipd.ira.uka.de/~prechelt/FAQ/neural-net-faq.html What is an Artificial Neural Network? http://www.emsl.pnl.gov:2080/docs/cie/neural/neural.ann.html Neural Nets homepages (indirect) http://www.emsl.pnl.gov:2080/docs/cie/neural/gateway.html Pasific Northwest National Laboratory link page to the neural network and neuro-science Wor
Over the past few years machine learning has seen a renewed explosion of interest, following a number of studies showing the effectiveness of neural networks in a range of tasks which had previously been considered incredibly hard. Neural networks' effectiveness in the fields of image recognition and natural language processing stems primarily from the vast amounts of data available to companies and researchers, coupled with the huge amounts of compute power available in modern accelerators such as GPUs, FP
Home Publications Musings CV When neural network representations converge Sander van Bree Postdoc JLU Giessen & MPI Leipzig Contact sandervanbree [at]gmail[.]com Curriculum Vitae When neural network representations converge November 8, 2024 In this blog post I explore possible theoretical implications of the finding that trained neural networks tend to show similar internal representations. I riff off of two recent papers; Chen & Bonner (2024) , and Huh et al. (2024) . In this blog post, I focus mostly on t
Where do the impressive performance gains of deep neural networks come from? Is their power due to the learning rules which adjust the connection weights or is it simply a function of the network architecture (i.e., many layers)? These two properties of networks are hard to disentangle. One way to tease apart the contributions of network architecture versus those of the learning regimen is to consider networks with randomised weights. To the extent that random networks show interesting behaviors, we can inf
Mechanistic interpretability aims to reverse engineer neural networks by uncovering which high-level algorithms they implement. Causal abstraction provides a precise notion of when a network
In this article, take a look at RNN vs autoregressive models see the vanishing gradient problem, see long-short term memory models, and more!