Contents
How to interpret a deep neural network model?
This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017.
Can a neural network interpret null feature values?
Unfortunately, while certain machine learning algorithms (such as XGBoost) can handle null feature values (i.e. not seeing a feature), neural networks can’t, so a slightly different approach will be needed to interpret them. The most common approach so far has been to consider the gradients of the inputs with respect to the predictions.
How to interpret a black box neural network?
Interpretable Neural Networks 1 Using gradients to interpret neural networks. Possibly the most intepretable model — and therefore the one we will use as inspiration — is a regression. 2 Integrated Gradients. 3 DeepLIFT. 4 Picking a baseline. 5 Conclusion. 6 Sources / further reading
Which is an advantage of interpretable neural networks?
The advantage is that it is fast — it only requires one backwards pass of the model to calculate feature importance values — and exact (since, unlike for integrated gradients, there is no approximation that takes place). As promised]
How does data normalization enable optimization of deep neural networks?
Data normalization enables efficient optimization of learning algorithms. Deep learning is simply a stack of layers that transform its input and pass it on to the next layer. Hence there is no good reason why one should restrict data normalization to the very first layer.
How is the Taylor decomposition used in deep neural networks?
The Taylor decomposition [9], [5] is a method that explains the model’s decision by decomposing the function value f(x) as a sum of relevance scores. The relevance scores are obtained by identification of the terms of a first-order Taylor expansion of the function at some root point x˜ for which f(x˜)=0.
Which is the best activation for deep learning?
ReLU became the default choice of activation for the Deep Learning toolkit. From an optimization viewpoint, it solves the long-standing issue of the so-called vanishing gradient. Optimizing neural networks is a highly non-convex problem. There are very few things that are more harmful than having a bad initialization of network weights.
https://www.youtube.com/watch?v=sKh12mB4P1c