2 research outputs found
Decision Explanation and Feature Importance for Invertible Networks
Deep neural networks are vulnerable to adversarial attacks and hard to
interpret because of their black-box nature. The recently proposed invertible
network is able to accurately reconstruct the inputs to a layer from its
outputs, thus has the potential to unravel the black-box model. An invertible
network classifier can be viewed as a two-stage model: (1) invertible
transformation from input space to the feature space; (2) a linear classifier
in the feature space. We can determine the decision boundary of a linear
classifier in the feature space; since the transform is invertible, we can
invert the decision boundary from the feature space to the input space.
Furthermore, we propose to determine the projection of a data point onto the
decision boundary, and define explanation as the difference between data and
its projection. Finally, we propose to locally approximate a neural network
with its first-order Taylor expansion, and define feature importance using a
local linear model. We provide the implementation of our method:
\url{https://github.com/juntang-zhuang/explain_invertible}.Comment: Correct notation
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
Smarter applications are making better use of the insights gleaned from data,
having an impact on every industry and research discipline. At the core of this
revolution lies the tools and the methods that are driving it, from processing
the massive piles of data generated each day to learning from and taking useful
action. Deep neural networks, along with advancements in classical ML and
scalable general-purpose GPU computing, have become critical components of
artificial intelligence, enabling many of these astounding breakthroughs and
lowering the barrier to adoption. Python continues to be the most preferred
language for scientific computing, data science, and machine learning, boosting
both performance and productivity by enabling the use of low-level libraries
and clean high-level APIs. This survey offers insight into the field of machine
learning with Python, taking a tour through important topics to identify some
of the core hardware and software paradigms that have enabled it. We cover
widely-used libraries and concepts, collected together for holistic comparison,
with the goal of educating the reader and driving the field of Python machine
learning forward.Comment: Preprint of a manuscript accepted for publication in "Machine
Learning with Python," a special issue of Information (ISSN 2078-2489