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Found 8 papers, 7 papers with code
Monotonic Neural Network: combining Deep Learning with Domain Knowledge for Chiller Plants Energy Optimization
In this paper, we are interested in building a domain knowledge based deep learning framework to solve the chiller plants energy optimization problems.
A zero-inflated gamma model for deconvolved calcium imaging traces
However, statistical modeling of deconvolved calcium signals (i. e., the estimated activity extracted by a pre-processing pipeline) is just as critical for interpreting calcium measurements, and for incorporating these observations into downstream probabilistic encoding and decoding models.
Short and Sparse Deconvolution --- A Geometric Approach
Short-and-sparse deconvolution (SaSD) is the problem of extracting localized, recurring motifs in signals with spatial or temporal structure.
Regression via Arbitrary Quantile Modeling
In the regression problem, L1 and L2 are the most commonly used loss functions, which produce mean predictions with different biases.
Short-and-Sparse Deconvolution -- A Geometric Approach
This paper is motivated by recent theoretical advances, which characterize the optimization landscape of a particular nonconvex formulation of SaSD.
Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data
Calcium imaging has revolutionized systems neuroscience, providing the ability to image large neural populations with single-cell resolution.
Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data
In vivo calcium imaging through microscopes has enabled deep brain imaging of previously inaccessible neuronal populations within the brains of freely moving subjects.
False discovery rate regression: an application to neural synchrony detection in primary visual cortex
But this may be inappropriate for many of today's large-scale screening problems, where auxiliary information about each test is often available, and where a combined analysis can lead to poorly calibrated error rates within different subsets of the experiment.
Methodology Applications
