Alexander Lavin | DeepAI

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∙ 11/20/2022

Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models

Soil and groundwater contamination is a pervasive problem at thousands o...

21 Lijing Wang, et al. ∙

∙ 08/17/2022

Physical Computing for Materials Acceleration Platforms

A ”technology lottery” describes a research idea or technology succeedin...

5 Erik Peterson, et al. ∙

∙ 05/20/2022

The Unreasonable Effectiveness of Deep Evidential Regression

There is a significant need for principled uncertainty reasoning in mach...

6 Nis Meinert, et al. ∙

∙ 12/06/2021

Simulation Intelligence: Towards a New Generation of Scientific Methods

The original "Seven Motifs" set forth a roadmap of essential methods for...

50 Alexander Lavin, et al. ∙

∙ 04/13/2021

Multivariate Deep Evidential Regression

There is significant need for principled uncertainty reasoning in machin...

0 Nis Meinert, et al. ∙

∙ 04/10/2021

Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization

As climate change increases the intensity of natural disasters, society ...

0 Björn Lütjens, et al. ∙

∙ 11/09/2020

Learnings from Frontier Development Lab and SpaceML – AI Accelerators for NASA and ESA

Research with AI and ML technologies lives in a variety of settings with...

5 Siddha Ganju, et al. ∙

∙ 10/16/2020

Physics-informed GANs for Coastal Flood Visualization

As climate change increases the intensity of natural disasters, society ...

0 Björn Lütjens, et al. ∙

∙ 09/16/2020

Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels

We present a probabilistic programmed deep kernel learning approach to p...

0 Alexander Lavin, et al. ∙

∙ 06/21/2020

Technology Readiness Levels for Machine Learning Systems

The development and deployment of machine learning systems can be execut...

0 Alexander Lavin, et al. ∙

∙ 06/19/2020

Manifolds for Unsupervised Visual Anomaly Detection

Anomalies are by definition rare, thus labeled examples are very limited...

0 Louise Naud, et al. ∙

∙ 05/23/2020

Fine-Grain Few-Shot Vision via Domain Knowledge as Hyperspherical Priors

Prototypical networks have been shown to perform well at few-shot learni...

0 Bijan Haney, et al. ∙

∙ 12/11/2018

Doubly Bayesian Optimization

Bayesian optimization (BO) is a powerful method for optimizing complex b...

0 Alexander Lavin, et al. ∙

∙ 08/03/2018

Cortical Microcircuits from a Generative Vision Model

Understanding the information processing roles of cortical circuits is a...

4 Dileep George, et al. ∙

∙ 03/24/2016

Clustering Time-Series Energy Data from Smart Meters

Investigations have been performed into using clustering methods in data...

0 Alexander Lavin, et al. ∙

∙ 11/03/2015

A Pareto Optimal D* Search Algorithm for Multiobjective Path Planning

Path planning is one of the most vital elements of mobile robotics, prov...

0 Alexander Lavin, et al. ∙

∙ 10/12/2015

Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark

Much of the world's data is streaming, time-series data, where anomalies...

0 Alexander Lavin, et al. ∙

∙ 05/22/2015

A Pareto Front-Based Multiobjective Path Planning Algorithm

Path planning is one of the most vital elements of mobile robotics. With...

0 Alexander Lavin, et al. ∙

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