34 lines
1.5 KiB
Markdown
34 lines
1.5 KiB
Markdown
The quick brown fox jumps over the lazy dog. The dog stays blissfully asleep. :)
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Robert recommended this topic space to me: formal methods applied to machine
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learning algorithims. The purpose is clear-cut: if we're able to use formal
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methods to prove things about machine learning based systems, we can make
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claims that their implementation in critical systems is safe and viable.
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This review seems to talk a good bit about that.
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# First Pass
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- Machine learned software is especially viable because it is more general
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than other types of programming. Things such as computer vision and decision
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making are very difficult to program manually relative to their ML counterpart.
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- Abstract interpretation can connect different formal methods that otherwise
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would seem disjoint, according to these authors.
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- They spend a lot of pages (~15) on *formal methods for neural networks*.
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There are two kinds.
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- **Complete Formal Methods** are sound and complete, and can provide
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counterexamples where appropriate. That being said, they are not usually
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sound on floating point arithmetic and often neglect rounding errors.
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- **Incomplete Formal Methods** are generally able to scale to larger
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neural networks, and are often sound, but generally suffer from false
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positives.
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- In comparison, they spend 2 pages on support vector machines and decision
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trees :( What did they ever do to you man!
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- They also mention that formal methods for data preparation and training are
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very much works in progress.
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