2.1k citations · 2.4k across the 15 of their papers we have counts for
4 papers · 1 filter
BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration
Augustus Odena, Kensen Shi, David Bieber +3
Program synthesis is challenging largely because of the difficulty of search in a large space of programs. Human programmers routinely tackle the task of writing complex programs b…
TF-Coder: Program Synthesis for Tensor Manipulations
Kensen Shi, David Bieber, Rishabh Singh
The success and popularity of deep learning is on the rise, partially due to powerful deep learning frameworks such as TensorFlow and PyTorch that make it easier to develop deep le…
Incremental Sampling Without Replacement for Sequence Models
Kensen Shi, David Bieber, Charles Sutton
Sampling is a fundamental technique, and sampling without replacement is often desirable when duplicate samples are not beneficial. Within machine learning, sampling is useful for…
Learning and Evaluating Contextual Embedding of Source Code
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan +1
Recent research has achieved impressive results on understanding and improving source code by building up on machine-learning techniques developed for natural languages. A signific…