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Found 5 papers, 3 papers with code
Geometry of Polynomial Neural Networks
The dimension serves as a geometric measure for the expressivity of the network, the learning degree is a measure for the complexity of training the network and provides upper bounds on the number of learnable functions.
3D genome reconstruction from partially phased Hi-C data
The 3-dimensional (3D) structure of the genome is of significant importance for many cellular processes.
Log-concave density estimation in undirected graphical models
We study the problem of maximum likelihood estimation of densities that are log-concave and lie in the graphical model corresponding to a given undirected graph $G$.
Identifying 3D Genome Organization in Diploid Organisms via Euclidean Distance Geometry
A standard approach is to transform the contact frequencies into noisy distance measurements and then apply semidefinite programming (SDP) formulations to obtain the 3D configuration.
The Model-Specific Markov Embedding Problem for Symmetric Group-Based Models
To showcase our main result on model embeddability, we provide an application to hachimoji models, which are eight-state models for synthetic DNA.
