Next paper candidates: 3 May

[m09]

Next paper candidates

Let's propose papers to study next! All papers mentioned in the comments of this issue will be listed in the next vote.

[m09]

Last session runner-up:

Graph Attention Networks by Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò and Yoshua Bengio

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training).

[m09]

Last session runner-up:

Bilateral Dependency Neural Networks for Cross-Language Algorithm Classification by Nghi D. Q. Bui, Yijun Yu and Lingxiao Jiang

Algorithm classification is to automatically identify the classes of a program based on the algorithm(s) and/or data structure(s) implemented in the program. It can be useful for various tasks, such as code reuse, code theft detection, and malware detection. Code similarity metrics, on the basis of features extracted from syntax and semantics, have been used to classify programs. Such features, however, often need manual selection effort and are specific to individual programming languages, limiting the classifiers to programs in the same language.

To recognise the similarities and differences among algorithms implemented in different languages, this paper describes a framework of Bilateral Neural Networks (Bi-NN) that builds a neural network on top of two underlying sub-networks, each of which encodes syntax and semantics of code in one language. A whole Bi-NN can be trained with bilateral programs that implement the same algorithms and/or data structures in different languages and then be applied to recognise algorithm classes across languages.

We have instantiated the framework with several kinds of token-, tree- and graph-based neural networks that encode and learn various kinds of information in code. We have applied the instances of the framework to a code corpus collected from GitHub containing thousands of Java and C++ programs implementing 50 different algorithms and data structures.

Our evaluation results show that the use of Bi-NN indeed produces promising algorithm classification results both within one language and across languages, and the encoding of dependencies from code into the underlying neural networks helps improve algorithm classification accuracy further. In particular, our custom-built dependency trees with tree-based convolutional neural networks achieve the highest classification accuracy among the different instances of the framework that we have evaluated.

Our study points to a possible future research direction to tailor bilateral and multilateral neural networks that encode more relevant semantics for code learning, mining and analysis tasks.

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Going to post a poll later today

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"Maybe Deep Neural Networks are the Best Choice for Modeling Source Code" by Rafael-Michael Karampatsis and Charles Sutton

Statistical language modeling techniques have successfully been applied to source code, yielding a variety of new software development tools, such as tools for code suggestion and improving readability. A major issue with these techniques is that code introduces new vocabulary at a far higher rate than natural language, as new identifier names proliferate. But traditional language models limit the vocabulary to a fixed set of common words. For code, this strong assumption has been shown to have a significant negative effect on predictive performance. But the open vocabulary version of the neural network language models for code have not been introduced in the literature. We present a new open-vocabulary neural language model for code that is not limited to a fixed vocabulary of identifier names. We employ a segmentation into subword units, subsequences of tokens chosen based on a compression criterion, following previous work in machine translation. Our network achieves best in class performance, outperforming even the state-of-the-art methods of Hellendoorn and Devanbu that are designed specifically to model code. Furthermore, we present a simple method for dynamically adapting the model to a new test project, resulting in increased performance. We showcase our methodology on code corpora in three different languages of over a billion tokens each, hundreds of times larger than in previous work. To our knowledge, this is the largest neural language model for code that has been reported.

[bzz]

A poll is out.

[bzz]

"Maybe Deep Neural Networks are the Best Choice for Modeling Source Code"

Is going to be read!