C++ code
26 papers with code • 0 benchmarks • 0 datasets
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Most implemented papers
A C Code Generator for Fast Inference and Simple Deployment of Convolutional Neural Networks on Resource Constrained Systems
iml130/nncg
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This paper presents a neural network code generator (NNCG) that generates from a trained CNN a plain ANSI C code file that encapsulates the inference in single a function.
Simulated Annealing Algorithm for the Multiple Choice Multidimensional Knapsack Problem
In this work, we present a simulated annealing based algorithm with open source C++ code to find good solutions to the multidimensional multiple choice knapsack problem.
CPL-SLAM: Efficient and Certifiably Correct Planar Graph-Based SLAM Using the Complex Number Representation
In this paper, we consider the problem of planar graph-based simultaneous localization and mapping (SLAM) that involves both poses of the autonomous agent and positions of observed landmarks.
DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs
In this work, we propose DORY (Deployment Oriented to memoRY) - an automatic tool to deploy DNNs on low cost MCUs with typically less than 1MB of on-chip SRAM memory.
Hybrid Deep Neural Networks to Infer State Models of Black-Box Systems
Our comparison with several traditional time series change point detection techniques showed that our approach improves their performance by up to 102%, in terms of finding state change points, measured by F1 score.
Break-It-Fix-It: Unsupervised Learning for Program Repair
To bridge this gap, we propose a new training approach, Break-It-Fix-It (BIFI), which has two key ideas: (i) we use the critic to check a fixer's output on real bad inputs and add good (fixed) outputs to the training data, and (ii) we train a breaker to generate realistic bad code from good code.
Latent Execution for Neural Program Synthesis
jungyhuk/latent-execution
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NeurIPS 2021
While recent works demonstrated limited success on domain-specific languages (DSL), it remains highly challenging to apply them to real-world programming languages, such as C. Due to complicated syntax and token variation, there are three major challenges: (1) unlike many DSLs, programs in languages like C need to compile first and are not executed via interpreters; (2) the program search space grows exponentially when the syntax and semantics of the programming language become more complex; and (3) collecting a large-scale dataset of real-world programs is non-trivial.
Learning C to x86 Translation: An Experiment in Neural Compilation
Deep learning has had a significant impact on many fields.
CryptOpt: Verified Compilation with Randomized Program Search for Cryptographic Primitives (full version)
Most software domains rely on compilers to translate high-level code to multiple different machine languages, with performance not too much worse than what developers would have the patience to write directly in assembly language.
Open-Source Skull Reconstruction with MONAI
project-monai/research-contributions
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The primary goal of this paper lies in the investigation of open-sourcing codes and pre-trained deep learning models under the MONAI framework.
