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Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
[article]
2020
arXiv
pre-print
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On the other hand, semidefinite programming (SDP) relaxations have successfully be applied to verification-agnostic networks, but do not currently scale beyond small networks due to poor time and space ...
By exploiting iterative eigenvector methods, we express all solver operations in terms of forward and backward passes through the network, enabling efficient use of hardware like GPUs/TPUs. ...
In contrast to LP-based approaches, the semidefinite programming (SDP) relaxation [52] has enabled robustness certification of verification-agnostic networks. ...
arXiv:2010.11645v2
fatcat:2z2gfv5kivd4bgq7zsd4nhwwaa
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2102.09479v1 fatcat:lcmjtahrird3nkeaqxe4zzocbm
Despite the promise of formal verification in ensuring the reliability of neural networks, progress in the direction of probabilistic specifications has been limited. ...
We show that an optimal choice of functional multipliers leads to exact verification (i.e., sound and complete verification), and for specific forms of multipliers, we develop tractable practical verification ...
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming. arXiv preprint arXiv:2010.11645, 2020.Dvijotham, K., Stanforth, R., Gowal, S., Mann, T. ...
arXiv:2102.09479v2
fatcat:becbh7ou7nevtebcrrhz2id65q
DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2106.09117v1 fatcat:yzrgrbtffjhi3pgkg3iw65xida
We demonstrate our method in obtaining tighter bounds on the worst-case performance of large convolutional networks in image classification and reinforcement learning settings. ...
Analyzing the worst-case performance of deep neural networks against input perturbations amounts to solving a large-scale non-convex optimization problem, for which several past works have proposed convex ...
Enabling certification of verification-agnostic networks via memory-efficient
semidefinite programming. arXiv preprint arXiv:2010.11645, 2020. ...
arXiv:2106.09117v2
fatcat:wmuclkb6yzdkzf24pz7s7wbrpe
Certified Defenses: Why Tighter Relaxations May Hurt Training
[article]
2021
arXiv
pre-print
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We believe the insights of this work can help drive the principled discovery of new and effective certified defense mechanisms. ...
The poor understanding of this paradox has forced recent state-of-the-art certified defenses to focus on designing various heuristics in order to mitigate its effects. ...
Enabling certification of verification-agnostic networks via memory-efficient
semidefinite programming. In NeurIPS, 2020. ...
arXiv:2102.06700v2
fatcat:f3rbasvxc5hdzjhbaolrbpmoea
Randomized Algorithms for Scientific Computing (RASC)
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2104.11079v1 fatcat:ynz5uw4ejrfx5fqfutuvg3ybyiOther Versions
challenges of complexity, robustness, and scalability. ...
This report summarizes the outcomes of that workshop, "Randomized Algorithms for Scientific Computing (RASC)," held virtually across four days in December 2020 and January 2021. ...
Inherent stochasticity in this and other networks could be exploited by proposing ways to speed up sampling, searching, and analysis of the network via randomized algorithms. (source: FEMA. ...
arXiv:2104.11079v2
fatcat:qwwowtufzvbfjaiotx733eexxe
Wireless Backhaul in 5G and Beyond: Issues, Challenges and Opportunities
[article]
2021
arXiv
pre-print
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With the projected densification of networks, wireless backhaul has become mandatory to use. There are still challenges to be tackled if wireless backhaul is to be used efficiently. ...
Resource allocation, deployment, scheduling, power management and energy efficiency are some of these problems. ...
The transmit side optimization problem is shown to be a non-convex quadratically constraint quadratic programs (QCQP) and is relaxed to a convex problem with the semidefinite program relaxation (SDR) method ...
arXiv:2103.08234v2
fatcat:3fug6wsmtrg5bfxk2ga2f2al5i
Modern applications of machine learning in quantum sciences
[article]
2022
arXiv
pre-print
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Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning. ...
We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback ...
Briegel, Lorenzo Cardarelli, and Kacper Cybiński for useful discussions and Fesido Studio Graficzne for the graphical design of the Lecture Notes. ...
arXiv:2204.04198v1
fatcat:rae77aetd5hahnovchru6kjbcy
Convex Optimization for Trajectory Generation
[article]
2021
arXiv
pre-print
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Reliable and efficient trajectory generation methods are a fundamental need for autonomous dynamical systems of tomorrow. ...
The goal of this article is to provide a comprehensive tutorial of three major convex optimization-based trajectory generation methods: lossless convexification (LCvx), and two sequential convex programming ...
“Robust control via se- [193] Morris W. Hirsch, Stephen Smale, and Robert L. Devaney.
quential semidefinite programming”. ...
arXiv:2106.09125v1
fatcat:owlktd5o7bhsrowgkmyz4kqu3y
Noisy intermediate-scale quantum (NISQ) algorithms
[article]
2021
arXiv
pre-print
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We additionally provide a comprehensive overview of various benchmarking and software tools useful for programming and testing NISQ devices. ...
A universal fault-tolerant quantum computer that can solve efficiently problems such as integer factorization and unstructured database search requires millions of qubits with low error rates and long ...
Moreover, the Lagrangian relaxation of the program 44 is a semidefinite program and efficiently solvable.
IV. THEORETICAL CHALLENGES
A. ...
arXiv:2101.08448v2
fatcat:eawbcaxikjeb7d3wqbjhutjdxy
Dagstuhl Reports, Volume 6, Issue 9, September 2016, Complete Issue
[article]
2017
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The organizers would like to express their gratitude to all participants of the Seminar. ...
Automata theory meets barrier certificates: Temporal logic verification of nonlinear systems. ...
This approach combines automatabased verification and the use of so-called barrier certificates. ...
doi:10.4230/dagrep.6.9
fatcat:eaxdsekvnrhuromxdk635sg5ai
50 Algebra in Computational Complexity (Dagstuhl Seminar 14391) Manindra Agrawal, Valentine Kabanets, Thomas Thierauf, and Christopher Umans 85 Privacy and Security in an Age of Surveillance (Dagstuhl Perspectives Workshop
unpublished
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Our algorithm is based on semidefinite programming. ...
We develop and extend certificate transparency, a proposal in this direction, so that it efficiently handles certificate revocation. ...
The computational power of high performance computing systems is growing exponentially, enabling finer grained scientific simulations. ...
fatcat:e5wnf6cuhvc3bceegijgut34zm
Dagstuhl Reports, Volume 7, Issue 8, August 2017, Complete Issue
[article]
2018
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The unique scientific atmosphere and the historic building provided not only all the room we needed for our program and the working groups, but also plenty of opportunities for continued discussions and ...
socializing outside the official program. ...
Interactive program synthesizers enable a user to communicate his/her intent via input-output examples. ...
doi:10.4230/dagrep.7.8
fatcat:gksmijgk5ff6reblxsqnt33aze
Chang_columbia_0054D_16362.pdf
[article]
2021
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, and an assimilation of different learning paradigms in autogenerative networks. ...
The object of this thesis is to study various challenges and applications of small-scale autogenerative networks in domains such as artificial life, reinforcement learning, neural network initialization ...
Hod Lipson, and the support of my colleagues in the Creative Machines Lab. ...
doi:10.7916/d8-3h0h-0c27
fatcat:gngf2rn365d4he4eylyo2gpape
ICAS 2012 Committee ICAS Advisory Chairs ICAS 2012 Technical Program Committee
unpublished
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The creation of such a broad and high quality conference program would not have been possible without their involvement. ...
We truly believe that, thanks to all these efforts, the final conference program consisted of top quality contributions. ...
Research Area initiative at the KTH Royal Institute of Technology, and by the Testbed for E2E Clouds RCLD-project funded by EIT ICT Labs. ...
fatcat:ctehnlqxqzfythqddv4uuzepza
Models and Efficient Algorithms for Convex Optimization under Uncertainty
2019
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Our framework considers both of these optimization under uncertainty modelsthrough a common lens of saddle point problems. ...
This dissertation examines various models for optimization under uncertainty, as well as efficient algorithms for solving such models that are scalable as the model size grows.We study three models for ...
a semidefinite program (SDP). ...
doi:10.1184/r1/9544625
fatcat:v3edev4ovbfzbcpq3rgsvrdnsi
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