Boolean Satisfiability (SAT) Problem/Satisfiability Modulo Theories (SMT) Solvers - Revision history

BPeat: Text replacement - "http://" to "https://"

2023-03-28T08:19:50Z

Text replacement - "http://" to "https://"

BPeat at 03:37, 27 September 2020

2020-09-27T03:37:14Z

BPeat at 23:19, 2 February 2019

2019-02-02T23:19:02Z

BPeat at 01:24, 6 July 2018

2018-07-06T01:24:07Z

BPeat: Created page with "[http://www.youtube.com/results?search_query=~SAT+SMT+Satisfiability+Modulo+Theories+Z3+Reluplex+Deep+Learning+Artificial+Intelligence Youtube search...] * [http://rise4fun.c..."

2018-07-06T01:23:17Z

Created page with "[http://www.youtube.com/results?search_query=~SAT+SMT+Satisfiability+Modulo+Theories+Z3+Reluplex+Deep+Learning+Artificial+Intelligence Youtube search...] * [http://rise4fun.c..."

New page

[http://www.youtube.com/results?search_query=~SAT+SMT+Satisfiability+Modulo+Theories+Z3+Reluplex+Deep+Learning+Artificial+Intelligence Youtube search...]

* [http://rise4fun.com/ Rise4Fun - automata concurrency design encoders infrastructure languages security synthesis testing verification language]
* [http://ijcai13.org/files/tutorial_slides/tb1.pdf SAT in AI: high performance search methods with applications]
* [http://stanford.edu/~guyk/pub/CAV2017_R.pdf Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks]

In what seems to be an endless back-and-forth between new adversarial attacks and new defenses against those attacks, we would like a means of formally verifying the robustness of machine learning algorithms to adversarial attacks. In the privacy domain, there is the idea of a differential privacy budget, which quantifies privacy over all possible attacks. In the following three papers, we see attempts at deriving an equivalent benchmark for security, one that will allow the evaluation of defenses against all possible attacks instead of just a specific one. [http://secml.github.io/class6/ Class 6: Measuring Robustness of ML Models]

* Nicholas Carlini, Guy Katz, Clark Barrett, David L. Dill. [http://arxiv.org/pdf/1709.10207.pdf Provably Minimally-Distorted Adversarial Examples] 20 Feb 2018

* Guy Katz, Clark Barrett, David Dill, Kyle Julian, Mykel Kochenderfer. [http://arxiv.org/pdf/1702.01135.pdf Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks] 19 May 2017

* Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, Luca Daniel. [http://arxiv.org/pdf/1801.10578.pdf Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach] 31 Jan 2018

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-[http://www.youtube.com/results?search_query=~SAT+SMT+Satisfiability+Modulo+Theories+Z3+Reluplex+Deep+Learning+Artificial+Intelligence Youtube search...]
+[https://www.youtube.com/results?search_query=~SAT+SMT+Satisfiability+Modulo+Theories+Z3+Reluplex+Deep+Learning+Artificial+Intelligence Youtube search...]
-[http://www.google.com/search?q=SAT+SMT+Satisfiability+Modulo+Theories+Z3+Reluplex+deep+machine+learning+ML+artificial+intelligence ...Google search]
+[https://www.google.com/search?q=SAT+SMT+Satisfiability+Modulo+Theories+Z3+Reluplex+deep+machine+learning+ML+artificial+intelligence ...Google search]
 * [[Offense - Adversarial Threats/Attacks]]
-* [http://rise4fun.com/ Rise4Fun - automata concurrency design encoders infrastructure languages security synthesis testing verification language]
+* [https://rise4fun.com/ Rise4Fun - automata concurrency design encoders infrastructure languages security synthesis testing verification language]
-* [http://ijcai13.org/files/tutorial_slides/tb1.pdf SAT in AI: high performance search methods with applications]
+* [https://ijcai13.org/files/tutorial_slides/tb1.pdf SAT in AI: high performance search methods with applications]
-* [http://stanford.edu/~guyk/pub/CAV2017_R.pdf Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks]
+* [https://stanford.edu/~guyk/pub/CAV2017_R.pdf Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks]
-In what seems to be an endless back-and-forth between new adversarial attacks and new defenses against those attacks, we would like a means of formally verifying the robustness of machine learning algorithms to adversarial attacks. In the [[privacy]] domain, there is the idea of a differential [[privacy]] budget, which quantifies [[privacy]] over all possible attacks. In the following three papers, we see attempts at deriving an equivalent benchmark for security, one that will allow the evaluation of defenses against all possible attacks instead of just a specific one. [http://secml.github.io/class6/ Class 6: Measuring Robustness of ML Models]
+In what seems to be an endless back-and-forth between new adversarial attacks and new defenses against those attacks, we would like a means of formally verifying the robustness of machine learning algorithms to adversarial attacks. In the [[privacy]] domain, there is the idea of a differential [[privacy]] budget, which quantifies [[privacy]] over all possible attacks. In the following three papers, we see attempts at deriving an equivalent benchmark for security, one that will allow the evaluation of defenses against all possible attacks instead of just a specific one. [https://secml.github.io/class6/ Class 6: Measuring Robustness of ML Models]
-* Nicholas Carlini, Guy Katz, Clark Barrett, David L. Dill. [http://arxiv.org/pdf/1709.10207.pdf Provably Minimally-Distorted Adversarial Examples] 20 Feb 2018
+* Nicholas Carlini, Guy Katz, Clark Barrett, David L. Dill. [https://arxiv.org/pdf/1709.10207.pdf Provably Minimally-Distorted Adversarial Examples] 20 Feb 2018
-* Guy Katz, Clark Barrett, David Dill, Kyle Julian, Mykel Kochenderfer. [http://arxiv.org/pdf/1702.01135.pdf Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks] 19 May 2017
+* Guy Katz, Clark Barrett, David Dill, Kyle Julian, Mykel Kochenderfer. [https://arxiv.org/pdf/1702.01135.pdf Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks] 19 May 2017
-* Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, Luca Daniel. [http://arxiv.org/pdf/1801.10578.pdf Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach] 31 Jan 2018
+* Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, Luca Daniel. [https://arxiv.org/pdf/1801.10578.pdf Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach] 31 Jan 2018
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← Older revision		Revision as of 03:37, 27 September 2020
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−	In what seems to be an endless back-and-forth between new adversarial attacks and new defenses against those attacks, we would like a means of formally verifying the robustness of machine learning algorithms to adversarial attacks. In the privacy domain, there is the idea of a differential privacy budget, which quantifies privacy over all possible attacks. In the following three papers, we see attempts at deriving an equivalent benchmark for security, one that will allow the evaluation of defenses against all possible attacks instead of just a specific one. [http://secml.github.io/class6/ Class 6: Measuring Robustness of ML Models]	+	In what seems to be an endless back-and-forth between new adversarial attacks and new defenses against those attacks, we would like a means of formally verifying the robustness of machine learning algorithms to adversarial attacks. In the [[privacy]] domain, there is the idea of a differential [[privacy]] budget, which quantifies [[privacy]] over all possible attacks. In the following three papers, we see attempts at deriving an equivalent benchmark for security, one that will allow the evaluation of defenses against all possible attacks instead of just a specific one. [http://secml.github.io/class6/ Class 6: Measuring Robustness of ML Models]

	* Nicholas Carlini, Guy Katz, Clark Barrett, David L. Dill. [http://arxiv.org/pdf/1709.10207.pdf Provably Minimally-Distorted Adversarial Examples] 20 Feb 2018		* Nicholas Carlini, Guy Katz, Clark Barrett, David L. Dill. [http://arxiv.org/pdf/1709.10207.pdf Provably Minimally-Distorted Adversarial Examples] 20 Feb 2018

← Older revision		Revision as of 23:19, 2 February 2019
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		+	{{#seo:
		+	\|title=PRIMO.ai
		+	\|titlemode=append
		+	\|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
		+	\|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
		+	}}
	[http://www.youtube.com/results?search_query=~SAT+SMT+Satisfiability+Modulo+Theories+Z3+Reluplex+Deep+Learning+Artificial+Intelligence Youtube search...]		[http://www.youtube.com/results?search_query=~SAT+SMT+Satisfiability+Modulo+Theories+Z3+Reluplex+Deep+Learning+Artificial+Intelligence Youtube search...]
		+	[http://www.google.com/search?q=SAT+SMT+Satisfiability+Modulo+Theories+Z3+Reluplex+deep+machine+learning+ML+artificial+intelligence ...Google search]

	* [[Offense - Adversarial Threats/Attacks]]		* [[Offense - Adversarial Threats/Attacks]]