Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) algorithm, whose high computational complexity makes the visualization of large real networks computationally prohibitive and traps large graphs into high energy configurations, resulting in hard-to-interpret “hairball” layouts. Here we use Graph Neural
Toggle navigation Alianna J. Maren Alianna J. Maren Statistical Mechanics, Neural Networks, Artificial Intelligence Selecting a Neural Network Transfer Function: Classic vs. Current Selecting a Neural Network Transfer Function: Classic vs. Current October 4, 2017 AJMaren Comments 2 comments Neural Network Transfer Functions: Sigmoid, Tanh, and ReLU Making it or breaking it with neural networks: how to make smart choices. Why We Weren’t Getting Convergence This last week, in working with a very simple and
Abstraction is a key verification technique to improve scalability. However, its use for neural networks is so far extremely limited. Previous approaches for abstracting classification networks replace several neurons with one of them that is similar enough. We can
Abstract page for arXiv paper 1911.08585: Ghost Units Yield Biologically Plausible Backprop in Deep Neural Networks
Miguel Aguilera's academic website. Complex Systems, Computational Neuroscience, Cognitive Systems, Artificial Life, Adaptive Behavior
If you're a developer who's been using TensorFlow for a while, you know that it's a powerful tool for creating neural networks. But what you may not know is
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You’ve now created your first Neural Network. It doesn’t work yet; we still have some more code to write but well done for getting here! We learned what a densely connected Neural Network is, and we created one using the TensorFlow Layers API. In the next lecture, we will cover how to train this mo
With the rise of deep neural networks, the challenge of explaining the predictions of these networks has become increasingly recognized. While many methods for explaining the decisions of deep neural networks exist, there is currently no consensus on how to evaluate them. On the other hand, robustness is a popular topic for deep learning research; however, it is hardly talked about in explainability until very recently. In this tutorial paper, we start by presenting gradient-based interpretability methods
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