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Found 8 papers, 6 papers with code
Towards fully covariant machine learning
We discuss links to causal modeling, and argue that the implementation of passive symmetries is particularly valuable when the goal of the learning problem is to generalize out of sample.
Dimensionless machine learning: Imposing exact units equivariance
Units equivariance (or units covariance) is the exact symmetry that follows from the requirement that relationships among measured quantities of physics relevance must obey self-consistent dimensional scalings.
A simple equivariant machine learning method for dynamics based on scalars
Physical systems obey strict symmetry principles.
Scalars are universal: Equivariant machine learning, structured like classical physics
There has been enormous progress in the last few years in designing neural networks that respect the fundamental symmetries and coordinate freedoms of physical law.
Dynamic estimation with random forests for discrete-time survival data
Time-varying covariates are often available in survival studies and estimation of the hazard function needs to be updated as new information becomes available.
Methodology Applications
Ensemble methods for survival function estimation with time-varying covariates
We compare their performance with that of the Cox model and transformation forest, adapted here to accommodate time-varying covariates, through a comprehensive simulation study in which the Kaplan-Meier estimate serves as a benchmark, and performance is compared using the integrated L2 difference between the true and estimated survival functions.
Experimental performance of graph neural networks on random instances of max-cut
In particular we consider Graph Neural Networks (GNNs) -- a class of neural networks designed to learn functions on graphs -- and we apply them to the max-cut problem on random regular graphs.
YASS: Yet Another Spike Sorter
Spike sorting is a critical first step in extracting neural signals from large-scale electrophysiological data.
