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Found 5 papers, 4 papers with code
Multimodal Phased Transformer for Sentiment Analysis
We evaluate our model with three sentiment analysis datasets and achieve comparable or superior performance compared with the existing methods, with a 90% reduction in the number of parameters.
Implicit Feature Decoupling with Depthwise Quantization
We use DQ in the context of Hierarchical Auto-Encoder and train end-to-end on an image feature representation.
Graph Conditioned Sparse-Attention for Improved Source Code Understanding
The fusion between a graph representation like Abstract Syntax Tree (AST) and a source code sequence makes the use of current approaches computationally intractable for large input sequence lengths.
GN-Transformer: Fusing Sequence and Graph Representation for Improved Code Summarization
SCG is the result of the early fusion between a source code snippet and the AST representation.
GN-Transformer: Fusing AST and Source Code information in Graph Networks
As opposed to natural languages, source code understanding is influenced by grammar relations between tokens regardless of their identifier name.
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