18 citations · 23 across the 9 of their papers we have counts for
5 papers · 1 filter
Measure Neural Mappings
Ricardo Baptista, Edoardo Calvello, Bohan Chen +2
We introduce and define measure neural mappings (MNMs), which combine measure-dependent neural transport with ensemble consistency for learning operators on probability measures. E…
Attention Kernels for Learning Maps Between Heavy-Tailed Measures
Kailen Hargenrader, Edoardo Calvello, Bohan Chen
Operator learning on probability measures can be accomplished with transformers. For measures with polynomial tails, the exponential weighting in softmax can make the corresponding…
One Operator for Many Densities: Amortized Approximation of Conditioning by Neural Operators
Panos Tsimpos, Edoardo Calvello, Ayoub Belhadji +1
Probabilistic conditioning is concerned with the identification of a distribution of a random variable given a random variable . It is a cornerstone of scientific and engine…
Formulation and Learning Of The Forecasting Problem
Edoardo Calvello, Elizabeth Carlson, Nikola Kovachki +2
Machine learning has opened new frontiers in purely data-driven forecasting of dynamical systems; the resulting methods are showing considerable promise in some application domains…
Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting
Jean Kossaifi, Nikola Kovachki, Morteza Mardani +15
The recent revolution in data-driven methods for weather forecasting has lead to a fragmented landscape of complex, bespoke architectures and training strategies, obscuring the fun…