Verified Deep Learning

Deep learning has transformed the way we think of software
and what it can do.
But deep neural networks are fragile and their behaviors are often surprising.
In many settings, we need to provide formal guarantees on the safety, security, correctness, or robustness of neural networks.
This
in-progress book covers foundational ideas from
formal verification and their application to reasoning about
deep learning.
You can get
all available chapters
as one pdf or access individual chapters below.
Keep in mind that the book is constantly evolving.
I Neural networks and correctness
II Constraint-based verification
III Abstraction-based verification
IV Verification and reinforcement learning
- Neural networks as policies
- Verifying RL policies
- Efficient neural policy verification
- Enforcing properties in RL
For comments, contact the
author.
Please use the following to cite this book.
@book{albarghouthi-book,
title = {Verified Deep Learning},
author = {Aws Albarghouthi},
publisher = {verifieddeeplearning.com},
note = {\url{http://verifieddeeplearning.com}},
year = {2020}
}
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