17 citations · 27 across the 9 of their papers we have counts for
8 papers
Parametric Sensitivities of a Wind-driven Baroclinic Ocean Using Neural Surrogates
Yixuan Sun, Elizabeth Cucuzzella, Steven Brus +6
Numerical models of the ocean and ice sheets are crucial for understanding and simulating the impact of greenhouse gases on the global climate. Oceanic processes affect phenomena s…
Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models
Yixuan Sun, Elizabeth Cucuzzella, Steven Brus +5
Modeling is crucial to understanding the effect of greenhouse gases, warming, and ice sheet melting on the ocean. At the same time, ocean processes affect phenomena such as hurrica…
Towards Continually Learning Application Performance Models
Ray A. O. Sinurat, Anurag Daram, Haryadi S. Gunawi +2
Machine learning-based performance models are increasingly being used to build critical job scheduling and application optimization decisions. Traditionally, these models assume th…
Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures
Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
The ability to learn continuously from an incoming data stream without catastrophic forgetting is critical to designing intelligent systems. Many approaches to continual learning r…
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
A. Ćiprijanović, A. Lewis, K. Pedro +4
Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to t…
AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architectures
Angel Yanguas-Gil, Sandeep Madireddy
In this work we have extended AutoML inspired approaches to the exploration and optimization of neuromorphic architectures. Through the integration of a parallel asynchronous model…