Live Talk, Zoom 2
Workshop: Learning Meaningful Representations of Life (LMRL)

Jackson Loper - Latent representations reveal that stationary covariances are always secretly linear


We recently found that any continuous covariance for time-series data, no matter how intricate, can be approximated arbitrarily well in terms of a well-behaved parametric family of linear projections of linear stochastic dynamical systems. This family makes efficient exact inference a breeze, even for millions of time-points. Applied to ATAC-seq data, this machinery infers smooth representations that encode how chromatin accessibility varies (1) along the one-dimensional topology of each chromosome and (2) throughout the diversity of cells.

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