71 citations · 179 across the 10 of their papers we have counts for
6 papers · 1 filter
Learnings from Frontier Development Lab and SpaceML -- AI Accelerators for NASA and ESA
Siddha Ganju, Anirudh Koul, Alexander Lavin +3
Research with AI and ML technologies lives in a variety of settings with often asynchronous goals and timelines: academic labs and government organizations pursue open-ended resear…
Physics-informed GANs for Coastal Flood Visualization
Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa +8
As climate change increases the intensity of natural disasters, society needs better tools for adaptation. Floods, for example, are the most frequent natural disaster, but during h…
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels
Alexander Lavin
We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural…
Manifolds for Unsupervised Visual Anomaly Detection
Louise Naud, Alexander Lavin
Anomalies are by definition rare, thus labeled examples are very limited or nonexistent, and likely do not cover unforeseen scenarios. Unsupervised learning methods that don't nece…
Technology Readiness Levels for AI & ML
Alexander Lavin, Gregory Renard
The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence…
Fine-Grain Few-Shot Vision via Domain Knowledge as Hyperspherical Priors
Bijan Haney, Alexander Lavin
Prototypical networks have been shown to perform well at few-shot learning tasks in computer vision. Yet these networks struggle when classes are very similar to each other (fine-g…