6 accepted papers
We deal with the combinatorial problem of learning directed acyclic graph (DAG) structure from observational data adhering to a linear structural equation model (SEM). Leveraging advances in differentiable, nonconvex characterizations of acyclicity, recent efforts have advocated a continuous constra…
Scanning Electron Microscopy (SEM) is a popular high resolution imaging modality for biological samples that has recently been applied to neural circuit reconstruction. For this application, relatively large volumes are imaged by repeatedly ablating away the exposed surface of the volume with a focu…
In this work we pose the parameter estimation of the Poisson-Gaussian noise model as a parametric model estimation problem. We first take patches from the image/video to analyze and treat variance stabilization transforms, e.g., the classical Generalized Anscombe transform, as a parametric model, wh…
Many neurons in the brain, such as place cells in the rodent hippocampus, have localized receptive fields, i.e., they respond to a small neighborhood of stimulus space. What is the functional significance of such representations and how can they arise? Here, we propose that localized receptive field…
In this work, we introduce a highly efficient algorithm to address the nonnegative matrix underapproximation (NMU) problem, i.e., nonnegative matrix factorization (NMF) with an additional underapproximation constraint. NMU results are interesting as, compared to traditional NMF, they present additio…
The short-time Fourier transform (STFT) is widely used to analyze the spectra of temporal signals that vary through time. Signals defined over graphs, due to their intrinsic complexity, exhibit large variations in their patterns. In this work we propose a new formulation for an STFT for signals defi…