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Found 11 papers, 4 papers with code
Relational dynamic memory networks
Neural networks excel in detecting regular patterns but are less successful in representing and manipulating complex data structures, possibly due to the lack of an external memory.
A deep tree-based model for software defect prediction
Defects are common in software systems and can potentially cause various problems to software users.
Software Engineering
Graph Memory Networks for Molecular Activity Prediction
GraphMem is capable of jointly training on multiple datasets by using a specific-task query fed to the controller as an input.
Graph Classification via Deep Learning with Virtual Nodes
The representation of the virtual node is then the representation of the entire graph.
Automatic feature learning for vulnerability prediction
Code flaws or vulnerabilities are prevalent in software systems and can potentially cause a variety of problems including deadlock, information loss, or system failure.
Software Engineering
One Size Fits Many: Column Bundle for Multi-X Learning
Much recent machine learning research has been directed towards leveraging shared statistics among labels, instances and data views, commonly referred to as multi-label, multi-instance and multi-view learning.
Column Networks for Collective Classification
CLN has many desirable theoretical properties: (i) it encodes multi-relations between any two instances; (ii) it is deep and compact, allowing complex functions to be approximated at the network level with a small set of free parameters; (iii) local and relational features are learned simultaneously; (iv) long-range, higher-order dependencies between instances are supported naturally; and (v) crucially, learning and inference are efficient, linear in the size of the network and the number of relations.
A deep learning model for estimating story points
Although there has been substantial research in software analytics for effort estimation in traditional software projects, little work has been done for estimation in agile projects, especially estimating user stories or issues.
Faster Training of Very Deep Networks Via p-Norm Gates
Gates are employed in many recent state-of-the-art recurrent models such as LSTM and GRU, and feedforward models such as Residual Nets and Highway Networks.
A deep language model for software code
Existing language models such as n-grams for software code often fail to capture a long context where dependent code elements scatter far apart.
DeepCare: A Deep Dynamic Memory Model for Predictive Medicine
We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes.
