Building High PDF

In this article, building High PDF treat existing interpretability methods as fundamental and composable building blocks for rich user interfaces. We find that these disparate techniques now come together in a unified grammar, fulfilling complementary roles in the resulting interfaces.

Författare: Maggie Freeman.
Collins Arabic Big Cat is a guided reading series for ages 3 to 11. The series is structured with reference to the learning progression of Arabic at nursery and primary schools researched especially for Collins. This carefully graded approach allows children to build up their reading knowledge of Arabic step by step.

Moreover, this grammar allows us to systematically explore the space of interpretability interfaces, enabling us to evaluate whether they meet particular goals. Our interfaces are speculative and one might wonder how reliable they are. Rather than address this point piecemeal, we dedicate a section to it at the end of the article. Arguably, this focus is due to the clear meaning these layers have: in computer vision, the input layer represents values for the red, green, and blue color channels for every pixel in the input image, while the output layer consists of class labels and their associated probabilities. In computer vision, we use neural networks that run the same feature detectors at every position in the image. We can think of each layer’s learned representation as a three-dimensional cube. Each cell in the cube is an activation, or the amount a neuron fires.

The cube of activations that a neural network for computer vision develops at each hidden layer. Different slices of the cube allow us to target the activations of individual neurons, spatial positions, or channels. To make a semantic dictionary, we pair every neuron activation with a visualization of that neuron and sort them by the magnitude of the activation. We use optimization-based feature visualization to avoid spurious correlation, but one could use other methods. Semantic dictionaries are powerful not just because they move away from meaningless indices, but because they express a neural network’s learned abstractions with canonical examples.