Deep Learning
xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery Fernando S. Paolo 1,, Tsu-ting Tim Lin
Identifying vessels that do not show up in conventional monitoring systems--known as "dark vessels"--is key to managing and securing the health
On the Relationship Between Variational Inference and Auto-Associative Memory
We then study how different neural network approaches to variational inference can be applied in this framework.
Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces Alexander Thebelt
Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search, as they achieve good predictive performance with little or no manual tuning, naturally handle discrete feature spaces, and are relatively insensitive to outliers in the training data.
Theoretical analysis of deep neural networks for temporally dependent observations
Despite the widespread use of neural networks in such settings, most theoretical developments of deep neural networks are under the assumption of independent observations, and theoretical results for temporally dependent observations are scarce.
PhysGNN: A Physics-Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image-Guided Neurosurgery
This task, however, poses a challenge.
Checklist
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