# First Pass
**Category:** 
This is a methods paper. 

**Context:** 
This paper proposes a way of using mixed integer linear programming (MILP)
to  evaluate properties of neural networks.

**Correctness:** 
Formal

**Contributions:** 
They do nifty things with bounds tightening and presolving that makes their
solver very fast compared to the state of the art or Reluplex. They also talk 
about stable and unstable neurons.

**Clarity:** 
They have a really good explanation of what a MILP problem is and how one
might encode a neural network as one.

# Second Pass
**What is the main thrust?**
The main thrust is their new solving method of MILPs for neural networks.
With their method, neural networks can have their neurons analyzed to 
prove whether or not the network is robust to input perturbations. This is 
especially important for classifiers, who need to know if there are sneaky
nonlinearities that can be harmful to a built system (like a glitch). This 
method of bounds tightening and MILP usage makes their solver much faster
and therein more capable to handle large networks.

**What is the supporting evidence?**
They have a whole bunch of experimental results.

**What are the key findings?**
MILPs and bound tightening is very good!

# Third Pass
**Recreation Notes:**

**Hidden Findings:**

**Weak Points? Strong Points?**
