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Found 5 papers, 2 papers with code
Improving Code Autocompletion with Transfer Learning
Software language models have achieved promising results predicting code completion usages, and several industry studies have described successful IDE integrations.
Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud
We propose a local-to-global representation learning algorithm for 3D point cloud data, which is appropriate to handle various geometric transformations, especially rotation, without explicit data augmentation with respect to the transformations.
Code Prediction by Feeding Trees to Transformers
We provide comprehensive experimental evaluation of our proposal, along with alternative design choices, on a standard Python dataset, as well as on a Python corpus internal to Facebook.
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Software Engineering
Weakly Supervised Instance Segmentation by Deep Community Learning
We present a weakly supervised instance segmentation algorithm based on deep community learning with multiple tasks.
Image-level Supervised Instance Segmentation
object-detection
+4
When Deep Learning Met Code Search
Our evaluation shows that: 1. adding supervision to an existing unsupervised technique can improve performance, though not necessarily by much; 2. simple networks for supervision can be more effective that more sophisticated sequence-based networks for code search; 3. while it is common to use docstrings to carry out supervision, there is a sizeable gap between the effectiveness of docstrings and a more query-appropriate supervision corpus.
