Search Results for author:
Found 8 papers, 5 papers with code
NL2KQL: From Natural Language to Kusto Query
The Kusto Query Language (KQL) is a widely used query language for large semi-structured data such as logs, telemetries, and time-series for big data analytics platforms.
TD-GEN: Graph Generation With Tree Decomposition
We propose TD-GEN, a graph generation framework based on tree decomposition, and introduce a reduced upper bound on the maximum number of decisions needed for graph generation.
Variational Learning with Disentanglement-PyTorch
Unsupervised learning of disentangled representations is an open problem in machine learning.
A Study into Echocardiography View Conversion
The size and length of the left ventricle in the generated target echo view is compared against that of the target ground-truth to assess the validity of the echo view conversion.
A Preliminary Study of Disentanglement With Insights on the Inadequacy of Metrics
However, a qualitative study of the encoded latents reveal that there is not a consistent correlation between the reported metrics and the disentanglement potential of the model.
GAN-enhanced Conditional Echocardiogram Generation
One of the most sought-after problems in echo is the segmentation of cardiac structures (e. g. chambers).
Variational Shape Completion for Virtual Planning of Jaw Reconstructive Surgery
The premorbid geometry of the mandible is of significant relevance in jaw reconstructive surgeries and occasionally unknown to the surgical team.
Muscle Excitation Estimation in Biomechanical Simulation Using NAF Reinforcement Learning
In this article, we propose a deep reinforcement learning method to estimate the muscle excitations in simulated biomechanical systems.
