57 lines
1.7 KiB
Markdown
57 lines
1.7 KiB
Markdown
---
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authors:
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- "Bunel, Rudy R"
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- "Turkaslan, Ilker"
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- "Torr, Philip"
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- "Kohli, Pushmeet"
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- "Mudigonda, Pawan K"
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citekey: "bunelUnifiedViewPiecewise2018"
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publish_date: 2018-01-01
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volume: 31
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publisher: "Curran Associates, Inc."
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last_import: 2025-05-12
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---
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# Indexing Information
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Published: 2018-01
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#ToRead
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>[!Abstract]
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>The success of Deep Learning and its potential use in many safety-critical
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applications has motivated research on formal verification of Neural Network
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(NN) models. Despite the reputation of learned NN models to behave as black
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boxes and the theoretical hardness of proving their properties, researchers
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have been successful in verifying some classes of models by exploiting their
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piecewise linear structure and taking insights from formal methods such as
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Satisifiability Modulo Theory. These methods are however still far from
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scaling to realistic neural networks. To facilitate progress on this crucial
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area, we make two key contributions. First, we present a unified framework
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that encompasses previous methods. This analysis results in the identification
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of new methods that combine the strengths of multiple existing approaches,
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accomplishing a speedup of two orders of magnitude compared to the previous
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state of the art. Second, we propose a new data set of benchmarks which
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includes a collection of previously released testcases. We use the benchmark
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to provide the first experimental comparison of existing algorithms and
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identify the factors impacting the hardness of verification problems.>[!seealso] Related Papers
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>
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# Annotations
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## Notes
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![[Paper Notes/A Unified View of Piecewise Linear Neural Network Verification.md]]
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## Highlights From Zotero
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## Follow-Ups
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