Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Timon Gehr"'
Publikováno v:
PLDI
Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation
Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation
Generative neural networks are powerful models capable of learning a wide range of rich semantic image transformations such as altering person's age, head orientation, adding mustache, changing the hair color and many more. At a high level, a generat
Publikováno v:
Proceedings of the ACM on Programming Languages, 3 (POPL)
We present a novel method for scalable and precise certification of deep neural networks. The key technical insight behind our approach is a new abstract domain which combines floating point polyhedra with intervals and is equipped with abstract tran
Publikováno v:
SIGCOMM
Not all important network properties need to be enforced all the time. Often, what matters instead is the fraction of time / probability these properties hold. Computing the probability of a property in a network relying on complex inter-dependent ro
Publikováno v:
PLDI
We present λPSI, the first probabilistic programming language and system that supports higher-order exact inference for probabilistic programs with first-class functions, nested inference and discrete, continuous and mixed random variables. λPSI’
Publikováno v:
PLDI
Existing quantum languages force the programmer to work at a low level of abstraction leading to unintuitive and cluttered code. A fundamental reason is that dropping temporary values from the program state requires explicitly applying quantum operat
Publikováno v:
PLDI
We present a novel approach for approximate sampling in probabilistic programs based on incremental inference. The key idea is to adapt the samples for a program P into samples for a program Q , thereby avoiding the expensive sampling computation for
Publikováno v:
Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 18)
PLDI
PLDI
Network operators often need to ensure that important probabilistic properties are met, such as that the probability of network congestion is below a certain threshold. Ensuring such properties is challenging and requires both a suitable language for
Publikováno v:
CCS
We present DP-Finder, a novel approach and system that automatically derives lower bounds on the differential privacy enforced by algorithms. Lower bounds are practically useful as they can show tightness of existing upper bounds or even identify inc
Publikováno v:
Programming Languages and Systems ISBN: 9783319898834
ESOP
ESOP
Probabilistic programming is an emerging technique for modeling processes involving uncertainty. Thus, it is important to ensure these programs are assigned precise formal semantics that also cleanly handle typical exceptions such as non-termination
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d10ee6797cd59a7e82b6068735541e05
https://doi.org/10.1007/978-3-319-89884-1_6
https://doi.org/10.1007/978-3-319-89884-1_6
Publikováno v:
CCS
Existing probabilistic privacy enforcement approaches permit the execution of a program that processes sensitive data only if the information it leaks is within the bounds specified by a given policy. Thus, to extract any information, users must manu