Two weeks ago, we began our machine study in earnest by constructing a full neural network. But this network was still quite simple by deep learning standards. In this article, we’re going to tackle a much more difficult problem: image recognition. Of course, we’ll still be using a well known data set with well-known results, so this is only the tip of the iceberg. We’ll be using the <a href="http://yann.lecun.com/exdb/mnist/" target="_blank">MNIST data set</a>. This set classifies images of
An overview of gradient descent in the context of neural networks. This is a method used widely throughout machine learning for optimizing how a computer performs on certain tasks
Do neural networks need explicit symbolic architecture to compose learned concepts, or can scaling alone enable compositional generalization? This asks whether compositionality is an architectural feature or an emergent property of scale
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A step-by-step explanation of how convolutional neural networks (CNNs) work, including convolutions, pooling layers, and dense layers with code examples
Abstract page for arXiv paper 2305.12578: Self-Explainable Graph Neural Networks for Link Prediction
Building neural networks from scratch is an enlightening journey through the intricacies of one of the most influential areas of machine learning. As the interest in neural networks continues to grow, so does the need for a comprehensive understanding of their fundamental concepts and inner workings
They're mandatory.
This work studies the design of neural networks that can process the weights or gradients of other neural networks, which we refer to as neural functional networks (NFNs). Despite a wide range of potential applications, including learned optimization, processing implicit neural representations, network editing, and policy evaluation, there are few unifying principles for designing effective architectures that process the weights of other networks. We approach the design of neural functionals through the len