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Meta-Analysis of the Functional Neuroimaging Literature with Probabilistic Logic Programming
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In: https://hal.archives-ouvertes.fr/hal-03590714 ; 2022 (2022)
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A gentle introduction to Girard's Transcendental Syntax for the linear logician
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In: https://hal.archives-ouvertes.fr/hal-02977750 ; 2022 (2022)
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PACSafe: Leveraging ARM Pointer Authentication for Memory Safety in C/C++ ...
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Compiler-Driven Simulation of Reconfigurable Hardware Accelerators ...
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How ISO C became unusable for operating systems development ...
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Quickstrom: Property Based Acceptance Testing with LTL Specifications ...
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| 9 |
POLYCRUISE: A Cross-Language Dynamic Information Flow Analysis ...
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POLYCRUISE: A Cross-Language Dynamic Information Flow Analysis ...
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| 11 |
A Systematic Evaluation of Large Language Models of Code ...
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| 12 |
On Reinforcement Learning, Effect Handlers, and the State Monad ...
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Compilable Neural Code Generation with Compiler Feedback ...
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Wang, Xin; Wang, Yasheng; Wan, Yao; Mi, Fei; Li, Yitong; Zhou, Pingyi; Liu, Jin; Wu, Hao; Jiang, Xin; Liu, Qun. - : arXiv, 2022
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Abstract:
Automatically generating compilable programs with (or without) natural language descriptions has always been a touchstone problem for computational linguistics and automated software engineering. Existing deep-learning approaches model code generation as text generation, either constrained by grammar structures in decoder, or driven by pre-trained language models on large-scale code corpus (e.g., CodeGPT, PLBART, and CodeT5). However, few of them account for compilability of the generated programs. To improve compilability of the generated programs, this paper proposes COMPCODER, a three-stage pipeline utilizing compiler feedback for compilable code generation, including language model fine-tuning, compilability reinforcement, and compilability discrimination. Comprehensive experiments on two code generation tasks demonstrate the effectiveness of our proposed approach, improving the success rate of compilation from 44.18 to 89.18 in code completion on average and from 70.3 to 96.2 in text-to-code generation, ... : Accepted by ACL 2022 ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Programming Languages cs.PL
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URL: https://arxiv.org/abs/2203.05132
https://dx.doi.org/10.48550/arxiv.2203.05132
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| 14 |
Active Learning Activities in a Collaborative Teacher Setting in Colours, Design and Visualisation
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In: Computers; Volume 11; Issue 5; Pages: 68 (2022)
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| 15 |
Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators
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| 17 |
Eine agentenbasierte Architektur für Programmierung mit gesprochener Sprache
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| 18 |
Concepts et sémantique des langages de programmation 2 : constructions modulaires et objet avec OCaml, Python, C++, Ada et Java
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In: https://hal.archives-ouvertes.fr/hal-03030511 ; ISTE Editions, 2021, 9781784057022 (2021)
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| 19 |
Multiplicative Linear Logic from Logic Programs and Tilings
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In: https://hal.archives-ouvertes.fr/hal-02895111 ; 2021 (2021)
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| 20 |
Language and semantics of expressions for Grafcet model synthesis in an MDE environment ; Langage et sémantique des expressions pour la synthèse de modèle Grafcet dans un environnement IDM
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In: https://hal.archives-ouvertes.fr/hal-02558838 ; 2021 (2021)
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