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Found 5 papers, 3 papers with code
On conditional diffusion models for PDE simulations
Modelling partial differential equations (PDEs) is of crucial importance in science and engineering, and it includes tasks ranging from forecasting to inverse problems, such as data assimilation.
Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data
Recently, transformer-based approaches have shown great promise in a range of weather forecasting problems.
In-Context In-Context Learning with Transformer Neural Processes
Standard NP architectures, such as the convolutional conditional NP (ConvCNP) or the family of transformer neural processes (TNPs), are not capable of in-context in-context learning, as they are only able to condition on a single dataset.
Approximately Equivariant Neural Processes
Our approach is agnostic to both the choice of symmetry group and model architecture, making it widely applicable.
Translation Equivariant Transformer Neural Processes
Notably, the posterior prediction maps for data that are stationary -- a common assumption in spatio-temporal modelling -- exhibit translation equivariance.
