You are all cordially invited to the second AMLab seminar this week, on Thursday November 1 at 16:00 in C3.163, where Stephan Alaniz will give a talk titled “Iterative Binary Decision”. Afterwards there are the usual drinks and snacks!
Abstract: The complexity of functions a neural network approximates make
it hard to explain what the classification decision is based on. In this
work, we present a framework that exposes more information about this
decision-making process. Instead of producing a classification in a
single step, our model iteratively makes binary sub-decisions which,
when combined as a whole, ultimately produce the same classification
result while revealing a decision tree as thought process. While there
is generally a trade-off between interpretability and accuracy, the
insights our model generates come at a negligible loss in accuracy. The
decision tree resulting from the sequence of binary decisions of our
model reveal a hierarchical clustering of the data and can be used as
learned attributes in zero-shot learning.