6 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.AI2023
Bridging Neural and Symbolic Representations with Transitional Dictionary Learning
Junyan Cheng, Peter Chin
This paper introduces a novel Transitional Dictionary Learning (TDL) framework that can implicitly learn symbolic knowledge, such as visual parts and relations, by reconstructing t…
cs.LG2021
Graph Conditioned Sparse-Attention for Improved Source Code Understanding
Junyan Cheng, Iordanis Fostiropoulos, Barry Boehm
Transformer architectures have been successfully used in learning source code representations. The fusion between a graph representation like Abstract Syntax Tree (AST) and a sourc…
cs.LG2021★ 6 cited
GN-Transformer: Fusing Sequence and Graph Representation for Improved Code Summarization
Junyan Cheng, Iordanis Fostiropoulos, Barry Boehm
As opposed to natural languages, source code understanding is influenced by grammatical relationships between tokens regardless of their identifier name. Graph representations of s…