2.1k citations · 2.8k across the 24 of their papers we have counts for
6 papers · 1 filter
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…
SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization
Ningyi Liao, Dingheng Mo, Siqiang Luo +2
Recent advances in data processing have stimulated the demand for learning graphs of very large scales. Graph Neural Networks (GNNs), being an emerging and powerful approach in sol…
Compositional Generalization and Decomposition in Neural Program Synthesis
Kensen Shi, Joey Hong, Manzil Zaheer +2
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…
Learning Structural Edits via Incremental Tree Transformations
Ziyu Yao, Frank F. Xu, Pengcheng Yin +2
While most neural generative models generate outputs in a single pass, the human creative process is usually one of iterative building and refinement. Recent work has proposed mode…
Learning to Represent Edits
Pengcheng Yin, Graham Neubig, Miltiadis Allamanis +2
We introduce the problem of learning distributed representations of edits. By combining a "neural editor" with an "edit encoder", our models learn to represent the salient informat…
Softmax Q-Distribution Estimation for Structured Prediction: A Theoretical Interpretation for RAML
Xuezhe Ma, Pengcheng Yin, Jingzhou Liu +2
Reward augmented maximum likelihood (RAML), a simple and effective learning framework to directly optimize towards the reward function in structured prediction tasks, has led to a…