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Found 6 papers, 2 papers with code
CodeExp: Explanatory Code Document Generation
Our experiments show that (1) our refined training dataset lets models achieve better performance in the explanation generation tasks compared to larger unrefined data (15x larger), and (2) fine-tuned models can generate well-structured long docstrings comparable to human-written ones.
Execution-based Evaluation for Data Science Code Generation Models
Code generation models can benefit data scientists' productivity by automatically generating code from context and text descriptions.
Learning Global and Local Consistent Representations for Unsupervised Image Retrieval via Deep Graph Diffusion Networks
Diffusion has shown great success in improving accuracy of unsupervised image retrieval systems by utilizing high-order structures of image manifold.
JUMPER: Learning When to Make Classification Decisions in Reading
Both the classification result and when to make the classification are part of the decision process, which is controlled by a policy network and trained with reinforcement learning.
Deep-learning Based Modeling of Fault Detachment Stability for Power Grid
The so-called "fail-delay cut-off" refers to the occurrence of N-1 backup protection action on the backbone network of the system, resulting in longer time for the removal of the fault.
Object-oriented Neural Programming (OONP) for Document Understanding
We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains.
