Open AccessProceedings Article
Convolutional neural networks over tree structures for programming language processing
TLDR
In this article, a tree-based convolutional neural network (TBCNN) is proposed for programming language processing, in which a convolution kernel is designed over programs' abstract syntax trees to capture structural information.Abstract:
Programming language processing (similar to natural language processing) is a hot research topic in the field of software engineering; it has also aroused growing interest in the artificial intelligence community. However, different from a natural language sentence, a program contains rich, explicit, and complicated structural information. Hence, traditional NLP models may be inappropriate for programs. In this paper, we propose a novel tree-based convolutional neural network (TBCNN) for programming language processing, in which a convolution kernel is designed over programs' abstract syntax trees to capture structural information. TBCNN is a generic architecture for programming language processing; our experiments show its effectiveness in two different program analysis tasks: classifying programs according to functionality, and detecting code snippets of certain patterns. TBCNN outperforms baseline methods, including several neural models for NLP.read more
Citations
More filters
Proceedings ArticleDOI
Deep code comment generation
TL;DR: DeepCom applies Natural Language Processing (NLP) techniques to learn from a large code corpus and generates comments from learned features for better comments generation of Java methods.
Posted Content
A Survey of Machine Learning for Big Code and Naturalness
TL;DR: This article presents a taxonomy based on the underlying design principles of each model and uses it to navigate the literature and discuss cross-cutting and application-specific challenges and opportunities.
Journal ArticleDOI
A Survey of Machine Learning for Big Code and Naturalness
TL;DR: A survey of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit the abundance of patterns of code.
Proceedings ArticleDOI
Deep code search
TL;DR: A novel deep neural network named CODEnn (Code-Description Embedding Neural Network) is proposed, which jointly embeds code snippets and natural language descriptions into a high-dimensional vector space, in such a way that code snippet and its corresponding description have similar vectors.
Proceedings Article
Discriminative embeddings of latent variable models for structured data
Hanjun Dai,Bo Dai,Le Song +2 more
TL;DR: Structured2vec as mentioned in this paper is an effective and scalable approach for structured data representation based on the idea of embedding latent variable models into feature spaces, and learning such feature spaces using discriminative information.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
Journal ArticleDOI
A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
Journal ArticleDOI
Representation Learning: A Review and New Perspectives
TL;DR: Recent work in the area of unsupervised feature learning and deep learning is reviewed, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks.
Book
Learning Deep Architectures for AI
TL;DR: The motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer modelssuch as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks are discussed.