Search Results for author:
Found 8 papers, 4 papers with code
ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedback
The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future.
GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph Alignment via Neighborhood Partitioning and Generative Subgraph Encoding
For example, can we query a LLM to identify the optimal contact in a professional network for a specific goal, based on relationships and attributes in a private database?
A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks
Such an approach enables us to propose a hyperbolic normalization layer and to further simplify the entire hyperbolic model to a Euclidean model cascaded with our hyperbolic normalization layer.
Graph Neural Network and Koopman Models for Learning Networked Dynamics: A Comparative Study on Power Grid Transients Prediction
In particular, accurate and timely prediction of the (electro-mechanical) transient dynamic trajectories of the power grid is necessary for early detection of any instability and prevention of catastrophic failures.
Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes
On the other hand, the aspects, entity and context, limit the answers by node-specific information and lead to higher precision and lower recall.
Knowledge Base Question Answering
Machine Reading Comprehension
Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks
Representation learning methods for heterogeneous networks produce a low-dimensional vector embedding for each node that is typically fixed for all tasks involving the node.
Snomed2Vec: Random Walk and Poincaré Embeddings of a Clinical Knowledge Base for Healthcare Analytics
Representation learning methods that transform encoded data (e. g., diagnosis and drug codes) into continuous vector spaces (i. e., vector embeddings) are critical for the application of deep learning in healthcare.
Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs
Link prediction, or predicting the likelihood of a link in a knowledge graph based on its existing state is a key research task.
