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Found 8 papers, 5 papers with code
Clifford-Steerable Convolutional Neural Networks
We present Clifford-Steerable Convolutional Neural Networks (CS-CNNs), a novel class of $\mathrm{E}(p, q)$-equivariant CNNs.
Clifford Group Equivariant Simplicial Message Passing Networks
Experimental results show that our method is able to outperform both equivariant and simplicial graph neural networks on a variety of geometric tasks.
Rolling Diffusion Models
Diffusion models have recently been increasingly applied to temporal data such as video, fluid mechanics simulations, or climate data.
On the Effectiveness of Hybrid Mutual Information Estimation
Estimating the mutual information from samples from a joint distribution is a challenging problem in both science and engineering.
Geometric Clifford Algebra Networks
GCANs are based on symmetry group transformations using geometric (Clifford) algebras.
Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study
We propose parameterizing the population distribution of the gravitational wave population modeling framework (Hierarchical Bayesian Analysis) with a normalizing flow.
Self-Supervised Inference in State-Space Models
Additionally, using an approximate conditional independence, we can perform smoothing without having to parameterize a separate model.
Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications
In this work we show how Bayesian modelling and the predictive uncertainty that it provides can be used to mitigate risk of misguided prediction and to detect out-of-domain examples in a medical setting.
