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Understanding Convolutions on Graphs
Understanding the building blocks and design choices of graph neural networks.
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30 articles
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A Gentle Introduction to Graph Neural Networks
What components are needed for building learning algorithms that leverage the structure and properties of graphs?
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Adversarial Reprogramming of Neural Cellular Automata
Reprogramming Neural CA to exhibit novel behaviour, using adversarial attacks.
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Weight Banding
Weights in the final layer of common visual models appear as horizontal bands. We investigate how and why.
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Branch Specialization
When a neural network layer is divided into multiple branches, neurons self-organize into coherent groupings.
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Multimodal Neurons in Artificial Neural Networks
We report the existence of multimodal neurons in artificial neural networks, similar to those found in the human brain.
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Self-Organising Textures
Neural Cellular Automata learn to generate textures, exhibiting surprising properties.
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Visualizing Weights
We present techniques for visualizing, contextualizing, and understanding neural network weights.
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Curve Circuits
Reverse engineering the curve detection algorithm from InceptionV1 and reimplementing it from scratch.
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High-Low Frequency Detectors
A family of early-vision neurons reacting to directional transitions from high to low spatial frequency.
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Naturally Occurring Equivariance in Neural Networks
Neural networks naturally learn many transformed copies of the same feature, connected by symmetric weights.
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Understanding RL Vision
With diverse environments, we can analyze, diagnose and edit deep reinforcement learning models using attribution.
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Communicating with Interactive Articles
Examining the design of interactive articles by synthesizing theory from disciplines such as education, journalism, and visualization.
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Thread: Differentiable Self-organizing Systems
A collection of articles and comments with the goal of understanding how to design robust and general purpose self-organizing systems.
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Self-classifying MNIST Digits
Training an end-to-end differentiable, self-organising cellular automata for classifying MNIST digits.
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Exploring Bayesian Optimization
How to tune hyperparameters for your machine learning model using Bayesian optimization.
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An Overview of Early Vision in InceptionV1
An overview of all the neurons in the first five layers of InceptionV1, organized into a taxonomy of 'neuron groups.'
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Visualizing Neural Networks with the Grand Tour
By focusing on linear dimensionality reduction, we show how to visualize many dynamic phenomena in neural networks.
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Thread: Circuits
What can we learn if we invest heavily in reverse engineering a single neural network?
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Zoom In: An Introduction to Circuits
By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks.
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Growing Neural Cellular Automata
Training an end-to-end differentiable, self-organising cellular automata model of morphogenesis, able to both grow and regenerate specific patterns.
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Visualizing the Impact of Feature Attribution Baselines
Exploring the baseline input hyperparameter, and how it impacts interpretations of neural network behavior.