Search Results for author:
Found 10 papers, 3 papers with code
Literature-based Discovery for Landscape Planning
This project demonstrates how medical corpus hypothesis generation, a knowledge discovery field of AI, can be used to derive new research angles for landscape and urban planners.
SmartChoices: Augmenting Software with Learned Implementations
We are living in a golden age of machine learning.
Accelerating COVID-19 research with graph mining and transformer-based learning
In 2020, the White House released the, "Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset," wherein artificial intelligence experts are asked to collect data and develop text mining techniques that can help the science community answer high-priority scientific questions related to COVID-19.
Accelerating Text Mining Using Domain-Specific Stop Word Lists
Eliminating domain-specific common words in a corpus reduces the dimensionality of the feature space, and improves the performance of text mining tasks.
Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization
In addition, supervised graph representation learning requires labeled data, which is expensive and error-prone.
CBAG: Conditional Biomedical Abstract Generation
We propose a transformer-based conditional language model with a shallow encoder "condition" stack, and a deep "language model" stack of multi-headed attention blocks.
AGATHA: Automatic Graph-mining And Transformer based Hypothesis generation Approach
Hypothesis generation systems address this challenge by mining the wealth of publicly available scientific information to predict plausible research directions.
Hypergraph Partitioning With Embeddings
As a result, hypergraph partitioning is an NP-Hard problem to both solve or approximate.
FOBE and HOBE: First- and High-Order Bipartite Embeddings
Typical graph embeddings may not capture type-specific bipartite graph features that arise in such areas as recommender systems, data visualization, and drug discovery.
Large-Scale Validation of Hypothesis Generation Systems via Candidate Ranking
This method evaluates a HG system by its ability to rank hypotheses by plausibility; a process reminiscent of human candidate selection.
