2.1k citations · 2.5k across the 27 of their papers we have counts for
28 papers
ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis
Kensen Shi, Joey Hong, Yinlin Deng +3
When writing programs, people have the ability to tackle a new complex task by decomposing it into smaller and more familiar subtasks. While it is difficult to measure whether neur…
A Probabilistic Framework for Modular Continual Learning
Lazar Valkov, Akash Srivastava, Swarat Chaudhuri +1
Modular approaches that use a different composition of modules for each problem are a promising direction in continual learning (CL). However, searching through the large, discrete…
LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas
Kensen Shi, Hanjun Dai, Wen-Ding Li +2
Search is an important technique in program synthesis that allows for adaptive strategies such as focusing on particular search directions based on execution results. Several prior…
Natural Language to Code Generation in Interactive Data Science Notebooks
Pengcheng Yin, Wen-Ding Li, Kefan Xiao +9
Computational notebooks, such as Jupyter notebooks, are interactive computing environments that are ubiquitous among data scientists to perform data wrangling and analytic tasks. T…
A Library for Representing Python Programs as Graphs for Machine Learning
David Bieber, Kensen Shi, Petros Maniatis +4
Graph representations of programs are commonly a central element of machine learning for code research. We introduce an open source Python library python_graphs that applies static…
Language Model Cascades
David Dohan, Winnie Xu, Aitor Lewkowycz +9
Prompted models have demonstrated impressive few-shot learning abilities. Repeated interactions at test-time with a single model, or the composition of multiple models together, fu…