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@ -26,7 +26,8 @@ network?
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**What is the main thrust?**
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**What is the main thrust?**
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This review gives an overview of formal methods applied to machine learning.
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This review gives an overview of formal methods applied to machine learning.
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Formal methods has been used for ML to test for robustness for various
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Formal methods has been used for ML to test for robustness for various
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perturbations on inputs. They start by talking about several types of formal methods, including deductive verification, design by refinement, proof
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perturbations on inputs. They start by talking about several types of formal
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methods, including deductive verification, design by refinement, proof
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assistants, model checking, and semantic static analysis, and then finally,
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assistants, model checking, and semantic static analysis, and then finally,
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abstract interpretation of semantic static analysis. The last one has been
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abstract interpretation of semantic static analysis. The last one has been
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used the most in FM of ML.
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used the most in FM of ML.
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@ -39,7 +40,14 @@ These methods generally do not apply well to large networks (beyond
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hundreds gets rough), but give the highest level of assurance. Here, there
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hundreds gets rough), but give the highest level of assurance. Here, there
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are two more subcategories: SMT based solvers and MILP-based solvers.
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are two more subcategories: SMT based solvers and MILP-based solvers.
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SMT solvers are Satisfiability Modulo Theory solvers. These solvers
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SMT solvers are Satisfiability Modulo Theory solvers. These solvers a set
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of constraints and check to see whether or not all of the constraints can be
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satisfied. Safety conditions are encoded as the *negation* of the safety constraint.
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That way, if a safety condition is violated, the SMT solver will pick it up as a
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counter example.
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MILP based solvers are Mixed Integer Linear Programming solvers. MILPs use
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linear programming where certain constraints are integers to
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**What is the supporting evidence?**
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**What is the supporting evidence?**
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