Jason Yosinski, "Good and bad assumptions in model design and interpretability"

The seduction of large neural nets is that one simply has to throw input data into a big network and magic comes out the other end. If the output is not magic enough, just add more layers. This simple approach works just well enough that it can lure us into a few bad assumptions, which we’ll discuss in this talk. One is that learning everything end-to-end is always best. We’ll look at an example where it isn’t. Another is that careful manual architecture design is useless because either one big stack of layers will work just fine, or if it doesn’t, we should just give up and use random architecture search and a bunch of computers. But perhaps we just need better tools and mental models to analyze the architectures we’re building; in this talk we’ll talk about one simple such tool. A final assumption is that as our models become large, they become inscrutable. This may turn out to be true for large models, but attempts at understanding persist, and in this talk, we’ll look at how the assumptions we put into our methods of interpretability color the results.