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The explosion of spatiotemporal data in the physical world requires new deep learning tools to model complex dynamical systems.
On 15 June, join CERN, Fermilab and Sanford Underground Research Facility (SURF) for an interactive livestream that will take you on a journey of the Deep Underground Neutrino Experiment (DUNE). The C...
In this talk, we will introduce some hyperspectral image classification methods based on deep learning architecture. Recently, deep learning-based hyperspectral image classification has attracted more...
In this talk, we discuss a unifying deep unfolding multi-sampling-ratio interpretable CS-MRI framework. The combined approach offers more generalizability than the existing deep-learning-based CS-MRI ...
Can humans endure long-term living in deep space?The answer is a lukewarm maybe, according to a new theory describing the complexity of maintaining gravity and oxygen, obtaining water, developing agri...
In this talk, I will present our recent work on the convergence/generalization analysis for the popular optimizers in deep learning. (1) We establish the convergence for Adam under (L0,L1 ) smoothness...
New research describes evidence that deep sea methane deposits change into gas more frequently than could be monitored previously and that a set of fossilized organisms has a unique ability to detect ...
Paula Rodríguez-Flores has always been obsessed with invertebrates. “Like really, really obsessed,” said the biodiversity postdoctoral fellow, who works in Harvard’s Museum of Comparative Zoology.
Conventional inferential methods for (deep) Gaussian Processes models can suffer from high computational complexity as they require large-scale operations with kernel matrices for training and inferen...
Tool path planning is a crucial factor of computer-aided design and manufacturing (CAD/CAM). To generate suitable tool paths, the previous methods often transform the problem into local or global opti...
Solving multi-scale PDEs is difficult in high-dimensional and/or convection-dominant cases. The interacting particle methods (IPM) are shown to outperform solving PDEs directly. Examples include compu...
Deep neural networks, as a powerful system to represent high dimensional complex functions, play a key role in deep learning. Convergence of deep neural networks is a fundamental issue in building the...
Since its first proposal in 2018, deep image prior has emerged as a very powerful unsupervised deep learning technique for solving inverse problems. The approach has demonstrated very encouraging empi...
Adaptive computation is of great importance in numerical simulations. The ideas for adaptive computations can be dated back to adaptive finite element methods in 1970s. In this talk, we shall first re...
What will be the impact to the ocean if humans are to mine the deep sea? It’s a question that’s gaining urgency as interest in marine minerals has grown.

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