no code implementations • 20 Nov 2022 • Lijing Wang, Takuya Kurihana, Aurelien Meray, Ilijana Mastilovic, Satyarth Praveen, Zexuan Xu, Milad Memarzadeh, Alexander Lavin, Haruko Wainwright
To quickly assess the spatiotemporal variations of groundwater contamination under uncertain climate disturbances, we developed a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Operator (U-FNO) to solve Partial Differential Equations (PDEs) of groundwater flow and transport simulations at the site scale. We develop a combined loss function that includes both data-driven factors and physical boundary constraints at multiple spatiotemporal scales.
no code implementations • 17 Aug 2022 • Erik Peterson, Alexander Lavin
A ''technology lottery'' describes a research idea or technology succeeding over others because it is suited to the available software and hardware, not necessarily because it is superior to alternative directions--examples abound, from the synergies of deep learning and GPUs to the disconnect of urban design and autonomous vehicles.
1 code implementation • 20 May 2022 • Nis Meinert, Jakob Gawlikowski, Alexander Lavin
There is a significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains.
1 code implementation • 6 Dec 2021 • Alexander Lavin, David Krakauer, Hector Zenil, Justin Gottschlich, Tim Mattson, Johann Brehmer, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, Atılım Güneş Baydin, Carina Prunkl, Brooks Paige, Olexandr Isayev, Erik Peterson, Peter L. McMahon, Jakob Macke, Kyle Cranmer, Jiaxin Zhang, Haruko Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer
We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence.
1 code implementation • 13 Apr 2021 • Nis Meinert, Alexander Lavin
We discuss three issues with a proposed solution to extract aleatoric and epistemic uncertainties from regression-based neural networks.
no code implementations • 10 Apr 2021 • Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa, Farrukh Chishtie, Natalia Díaz-Rodríguez, Océane Boulais, Aruna Sankaranarayanan, Margaux Masson-Forsythe, Aaron Piña, Yarin Gal, Chedy Raïssi, Alexander Lavin, Dava Newman
Our work aims to enable more visual communication of large-scale climate impacts via visualizing the output of coastal flood models as satellite imagery.
1 code implementation • 11 Jan 2021 • Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic, Siddha Ganju, Dava Newman, Atılım Güneş Baydin, Sujoy Ganguly, Danny Lange, Amit Sharma, Stephan Zheng, Eric P. Xing, Adam Gibson, James Parr, Chris Mattmann, Yarin Gal
The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
no code implementations • 9 Nov 2020 • Siddha Ganju, Anirudh Koul, Alexander Lavin, Josh Veitch-Michaelis, Meher Kasam, James Parr
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 research focusing on discoveries with long-term value, while research in industry is driven by commercial pursuits and hence focuses on short-term timelines and return on investment.
no code implementations • 16 Oct 2020 • Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa, Farrukh Chishtie, Natalia Díaz-Rodriguez, Océane Boulais, Aaron Piña, Dava Newman, Alexander Lavin, Yarin Gal, Chedy Raïssi
As climate change increases the intensity of natural disasters, society needs better tools for adaptation.
no code implementations • 16 Sep 2020 • Alexander Lavin
We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases.
no code implementations • 21 Jun 2020 • Alexander Lavin, Gregory Renard
Drawing on experience in both spacecraft engineering and AI/ML (from research through product), we propose a proven systems engineering approach for machine learning development and deployment.
1 code implementation • 19 Jun 2020 • Louise Naud, Alexander Lavin
Through theoretical and empirical explorations of manifold shapes, we develop a novel hyperspherical Variational Auto-Encoder (VAE) via stereographic projections with a gyroplane layer - a complete equivalent to the Poincar\'e VAE.
no code implementations • 23 May 2020 • Bijan Haney, Alexander Lavin
Prototypical networks have been shown to perform well at few-shot learning tasks in computer vision.
no code implementations • 11 Dec 2018 • Alexander Lavin
Probabilistic programming systems enable users to encode model structure and naturally reason about uncertainties, which can be leveraged towards improved Bayesian optimization (BO) methods.
no code implementations • 3 Aug 2018 • Dileep George, Alexander Lavin, J. Swaroop Guntupalli, David Mely, Nick Hay, Miguel Lazaro-Gredilla
Understanding the information processing roles of cortical circuits is an outstanding problem in neuroscience and artificial intelligence.
no code implementations • 24 Mar 2016 • Alexander Lavin, Diego Klabjan
Investigations have been performed into using clustering methods in data mining time-series data from smart meters.
no code implementations • 3 Nov 2015 • Alexander Lavin
The global path plan can be calculated with a variety of informed search algorithms, most notably the A* search method, guaranteed to deliver a complete and optimal solution that minimizes the path cost.
3 code implementations • 12 Oct 2015 • Alexander Lavin, Subutai Ahmad
Here we propose the Numenta Anomaly Benchmark (NAB), which attempts to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data.
Ranked #4 on Anomaly Detection on Numenta Anomaly Benchmark
no code implementations • 22 May 2015 • Alexander Lavin
Path planning is one of the most vital elements of mobile robotics.