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Learning to superoptimize programs - Workshop Version [article]

Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H.S.Torr, Pushmeet Kohli
2016 arXiv   pre-print
Superoptimization requires the estimation of the best program for a given computational task. In order to deal with large programs, superoptimization techniques perform a stochastic search.  ...  To alleviate this deficiency, we learn a proposal distribution over possible modifications using Reinforcement Learning.  ...  Introduction Superoptimization requires us to obtain the optimal program for a computational task.  ... 
arXiv:1612.01094v1 fatcat:rjlyc6cflreqhi3p5jlout6ihm

Towards a Probabilistic Version of Bidirectional OT Syntax and Semantics

K. van Deemter
2004 Journal of Semantics  
The ideas outlined in this paper apply to interpretation as well as generation, but particular attention will be given to the question how bidirectionality can be put to use in Natural Language Generation  ...  Kees van Deemter ITRI, University of Brighton Kees. van.Deemter@itri.brighton.ac.uk This paper argues that a purely probabilistic version of bidirectional Optimality-Theoretic syntax and semantics (Blutner  ...  'How we learn variation, optionality, and prob-  ... 
doi:10.1093/jos/21.3.251 fatcat:jv4p6eotjzfyhdlrt3eciuwryi

Performance search engine driven by prior knowledge of optimization

Youngsung Kim, Pavol Černý, John Dennis
2015 Proceedings of the 2nd ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming - ARRAY 2015  
For scientific array-based programs, optimization for a particular target platform is a hard problem.  ...  We provide (i) a search-space description language, which enables the user to describe optimization options to be used; (ii) search engine that enables testing the performance impact of optimization options  ...  One approach to program synthesis is superoptimization [15] . Superoptimization is the task of finding the optimal code sequence for a single, loop-free sequence of instructions.  ... 
doi:10.1145/2774959.2774963 dblp:conf/pldi/KimCD15 fatcat:dudrh6rl6vawzkesiwothzqz64

Deep Learning Software Engineering: State of Research and Future Directions [article]

Prem Devanbu, Matthew Dwyer, Sebastian Elbaum, Michael Lowry, Kevin Moran, Denys Poshyvanyk, Baishakhi Ray, Rishabh Singh, Xiangyu Zhang
2020 arXiv   pre-print
The goal of this workshop was to outline high priority areas for cross-cutting research.  ...  Given the current transformative potential of research that sits at the intersection of Deep Learning (DL) and Software Engineering (SE), an NSF-sponsored community workshop was conducted in co-location  ...  Some near term opportunities may lie in the areas of: • Program Superoptimization -automatically generating programs that are functionally equivalent to a given implementation but allow for complete transformation  ... 
arXiv:2009.08525v1 fatcat:w3lt3j6iavdx5df6xvwyvz6xsm

Report of the Workshop on Program Synthesis for Scientific Computing [article]

Hal Finkel, Ignacio Laguna
2021 arXiv   pre-print
This report is the result of the Workshop on Program Synthesis for Scientific Computing was held virtually on August 4-5 2020 (https://prog-synth-science.github.io/2020/).  ...  This report reviews the relevant areas of program synthesis work for scientific computing, discusses successes to date, and outlines opportunities for future work.  ...  This workshop expands on these reports by exploring how program synthesis, an approach to automatically generating software programs based on some user intent [35, 36] -along with other high-level, AI-integrated  ... 
arXiv:2102.01687v1 fatcat:vaq33ohgq5aczmiq5rci5hbpdi

Booby trapping software

Stephen Crane, Per Larsen, Stefan Brunthaler, Michael Franz
2013 Proceedings of the 2013 workshop on New security paradigms workshop - NSPW '13  
This extends the arsenal of weaponry available to defenders with an active technique for directly reacting to attacks.  ...  Defenders are constantly patching the newest hole in their defenses and creating taller and thicker walls, without placing guards on those walls to watch for the enemy and react to attacks.  ...  We especially thank our reviewers, shepherd Daniela Oliveira, and all the workshop participants for their valuable contributions.  ... 
doi:10.1145/2535813.2535824 dblp:conf/nspw/CraneLBF13 fatcat:svu5n5qqiza4tijecybnl5m2ni

Data-Driven Synthesis of Full Probabilistic Programs [chapter]

Sarah Chasins, Phitchaya Mangpo Phothilimthana
2017 Lecture Notes in Computer Science  
We introduce a data-guided approach to the program mutation stage of simulated annealing; this innovation allows our tool to scale to synthesizing complete probabilistic programs from scratch.  ...  Our synthesizer leverages the input data to generate a program sketch, then applies simulated annealing to complete the sketch.  ...  Department of Energy, Office of Science, Office of Basic Energy Sciences Energy Frontier Research Centers program under Award Number FOA-0000619, and grants from DARPA FA8750-14-C-0011 and DARPA FA8750  ... 
doi:10.1007/978-3-319-63387-9_14 fatcat:idnaf7svwrc2thcmndstn6gjxm

Derive

Dawson R. Engler, Wilson C. Hsieh
2000 Proceedings of the ACM SIGPLAN workshop on Dynamic and adaptive compilation and optimization - DYNAMO '00  
Many binary tools, such as disassemblers, dynamic code generation systems, and executable code rewriters, need to understand how machine instructions are encoded.  ...  This paper discusses our current derive prototype, explains how it computes instruction encodings, and also discusses the more general implications of the ability to extract functionality from installed  ...  A related area of machine learning research is called inductive logic programming" 1 . Its goals are similar to ours: to build tools that can automatically reason about other programs.  ... 
doi:10.1145/351397.351409 dblp:conf/dynamo/EnglerH00 fatcat:z3xhk6kb3fgvdcyxny3slktsua

Loki: Hardening Code Obfuscation Against Automated Attacks [article]

Moritz Schloegel, Tim Blazytko, Moritz Contag, Cornelius Aschermann, Julius Basler, Thorsten Holz, Ali Abbasi
2021 arXiv   pre-print
In general, they suffer from focusing on a single attack vector, allowing an attacker to switch to other more effective techniques, such as program synthesis.  ...  Moreover, Loki protects against previously unaccounted attack vectors such as program synthesis, for which it reduces the success rate to merely 19%.  ...  In International Workshop on Information Security Applications, 2007.  ... 
arXiv:2106.08913v2 fatcat:lbophyz6snhqdgxgfdf5nv2jja

Sound, Precise, and Fast Abstract Interpretation with Tristate Numbers [article]

Harishankar Vishwanathan, Matan Shachnai, Srinivas Narayana, Santosh Nagarakatte
2021 arXiv   pre-print
Among these, the domain of tnums (tristate numbers) is a key domain used to reason about the bitwise uncertainty in program values.  ...  To ensure that user code is safe to run in kernel context, BPF relies on a static analyzer that proves properties about the code, such as bounded memory access and the absence of operations that crash.  ...  Evolution and lessons on Principles of programming languages, 1977, pp. 238–252. learned.” in Linux Plumbers Conference, 2018. [39] E. Cree, Y.  ... 
arXiv:2105.05398v3 fatcat:uvxlmnvf35h5pbjw45y7n436mi

Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing [article]

Johannes Betz, Hongrui Zheng, Alexander Liniger, Ugo Rosolia, Phillip Karle, Madhur Behl, Venkat Krovi, Rahul Mangharam
2022 arXiv   pre-print
We focus on the field of autonomous racecars only and display the algorithms, methods and approaches that are used in the fields of perception, planning and control as well as end-to-end learning.  ...  Further, with an increasing number of autonomous racing competitions, researchers now have access to a range of high performance platforms to test and evaluate their autonomy algorithms.  ...  [104] use Sequential Quadratic Programming (SQP) to solve the non-linear programming problem where lap time is the objective. Rucco et al.  ... 
arXiv:2202.07008v1 fatcat:hwhp43thevd2bighs7y7j7qnam

Resolving Relative Time Expressions in Dutch Text with Constraint Handling Rules [chapter]

Matje van de Camp, Henning Christiansen
2013 Lecture Notes in Computer Science  
Checking induced entries over an artificial dataset generated using a known grammar demonstrates that the method learns lexical entries compatible with those defined by linguists, with different versions  ...  We introduce a method for data-driven learning of lexical entries in an inherently incremental semantic grammar formalism, Dynamic Syntax (DS).  ...  We are thankful to the International Computer Science Institute (University of California at Berkeley) and, in particular, to the people on the Neural Theory of Language project.  ... 
doi:10.1007/978-3-642-41578-4_10 fatcat:rvzj2l4fnvg77aepokghncuhui

Optimal Neural Program Synthesis from Multimodal Specifications

Xi Ye, Qiaochu Chen, Isil Dillig, Greg Durrett
2021 Findings of the Association for Computational Linguistics: EMNLP 2021   unpublished
Learning to infer with complex structures. In Proceedings of the An- program sketches.  ...  Learning to DARPA Workshop on Speech and Natural Language. mine aligned code and natural language pairs from stack overflow.  ... 
doi:10.18653/v1/2021.findings-emnlp.146 fatcat:5hdt7wph6vcepjwqpbgmvjozsq

Globalizing Domain-Specific Languages (Dagstuhl Seminar 14412) Optimal Algorithms and Proofs (Dagstuhl Seminar 14421) Modeling, Verification, and Control of Complex Systems for Energy Networks (Dagstuhl Seminar 14441) Symbolic Execution and Constraint Solving (Dagstuhl Seminar 14442)

Luc De Raedt, Siegfried Nijssen, Barry O'sullivan, Michele, Betty Cheng, Benoit Combemale, Robert France, Jean-Marc Jézéquel, Bernhard Rumpe, Olaf Beyersdorff, Edward Hirsch, Jan Krajíček (+9 others)
2014 Constraints   unpublished
In particular, symbolic execution helps to glean the intended program behavior via analysis of the buggy trace, analysis of other traces or other program versions.  ...  mining and learning, modelling languages, which allow to declaratively specify and solve mining and learning problems, and programming languages, that support the learning of functions and subroutines.  ...  Overall, we found these short-talk sessions to be engaging and fun. We encourage future workshop organizers to include similar sessions in their meetings.  ... 
fatcat:zxrfsk6qcjharpg4vb5hosp4bu

LIPIcs, Volume 32, SNAPL'15, Complete Volume [article]

Thomas Ball, Rastislav Bodik, Shriram Krishnamurthi, Benjamin S. Lerner, Greg Morrisett
2015
The writing of this paper was encouraged by discussions led during Dagstuhl Seminar 14211; the Summit on Advances in Programming Languages was brought to the author's attention at Dagstuhl Seminar 14412  ...  The gradual guarantee is sketched in Boyland's paper at the FOOL workshop [9] and in Siek's presentation at the NII Shonan meeting on Contracts [48]. Acknowledgements.  ...  For instance, STOKE [27] uses stochastic search to solve the superoptimization problem.  ... 
doi:10.4230/lipics.snapl.2015 fatcat:ljoxwa7wp5achn2tbrmpriez7y
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