Search Results for author:
Found 7 papers, 1 papers with code
Retrosynthesis Prediction via Search in (Hyper) Graph
As a semi-template-based method RetroSiG has several advantages.
Deep Reinforcement Learning for Quantitative Trading
Incorporating deep reinforcement learning (DRL) with imitative learning methodologies, we bolster the proficiency of our model.
Use neural networks to recognize students' handwritten letters and incorrect symbols
Correcting students' multiple-choice answers is a repetitive and mechanical task that can be considered an image multi-classification task.
RCsearcher: Reaction Center Identification in Retrosynthesis via Deep Q-Learning
The critical insight in this framework is that the single or multiple reaction center must be a node-induced subgraph of the molecular product graph.
More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference
Our critical insight into INFMCS is the strong correlation between similarity score and Maximum Common Subgraph (MCS).
Sub-GMN: The Neural Subgraph Matching Network Model
Comparing with the previous GNNs-based methods for subgraph matching task, our proposed Sub-GMN allows varying query and data graphes in the test/application stage, while most previous GNNs-based methods can only find a matched subgraph in the data graph during the test/application for the same query graph used in the training stage.
Global-aware Beam Search for Neural Abstractive Summarization
A global scoring mechanism is then developed to regulate beam search to generate summaries in a near-global optimal fashion.
