416 citations · 581 across the 5 of their papers we have counts for
10 papers
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Shuai Lu, Daya Guo, Shuo Ren +19
Benchmark datasets have a significant impact on accelerating research in programming language tasks. In this paper, we introduce CodeXGLUE, a benchmark dataset to foster machine le…
PyMT5: multi-mode translation of natural language and Python code with transformers
Colin B. Clement, Dawn Drain, Jonathan Timcheck +2
Simultaneously modeling source code and natural language has many exciting applications in automated software development and understanding. Pursuant to achieving such technology,…
Fully Convolutional Spatio-Temporal Models for Representation Learning in Plasma Science
Ge Dong, Kyle Gerard Felker, Alexey Svyatkovskiy +2
We have trained a fully convolutional spatio-temporal model for fast and accurate representation learning in the challenging exemplar application area of fusion energy plasma scien…
IntelliCode Compose: Code Generation Using Transformer
Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu +1
In software development through integrated development environments (IDEs), code completion is one of the most widely used features. Nevertheless, majority of integrated developmen…
Fast and Memory-Efficient Neural Code Completion
Alexey Svyatkovskiy, Sebastian Lee, Anna Hadjitofi +3
Code completion is one of the most widely used features of modern integrated development environments (IDEs). While deep learning has made significant progress in the statistical p…
Training Distributed Deep Recurrent Neural Networks with Mixed Precision on GPU Clusters
Alexey Svyatkovskiy, Julian Kates-Harbeck, William Tang
In this paper, we evaluate training of deep recurrent neural networks with half-precision floats. We implement a distributed, data-parallel, synchronous training algorithm by integ…