Communicating with Interactive Articles
Examining the design of interactive articles by synthesizing theory from disciplines such as education, journalism, and visualization.
Examining the design of interactive articles by synthesizing theory from disciplines such as education, journalism, and visualization.
Training an end-to-end differentiable, self-organising cellular automata for classifying MNIST digits.
A collection of articles and comments with the goal of understanding how to design robust and general purpose self-organizing systems.
Part one of a three part deep dive into the curve neuron family.
How to tune hyperparameters for your machine learning model using Bayesian optimization.
An overview of all the neurons in the first five layers of InceptionV1, organized into a taxonomy of 'neuron groups.'
By focusing on linear dimensionality reduction, we show how to visualize many dynamic phenomena in neural networks.
By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks.
What can we learn if we invest heavily in reverse engineering a single neural network?
Training an end-to-end differentiable, self-organising cellular automata model of morphogenesis, able to both grow and regenerate specific patterns.
Exploring the baseline input hyperparameter, and how it impacts interpretations of neural network behavior.
Detailed derivations and open-source code to analyze the receptive fields of convnets.