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Found 6 papers, 3 papers with code
Fast Grid Emissions Sensitivities using Parallel Decentralized Implicit Differentiation
Marginal emissions rates -- the sensitivity of carbon emissions to electricity demand -- are important for evaluating the impact of emissions mitigation measures.
Dynamic locational marginal emissions via implicit differentiation
The method is model agnostic; it can compute LMEs for any convex optimization-based dispatch model, including some of the complex dispatch models employed by system operators in real electricity systems.
Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models
This construction is similar to Bayesian nonparametric mixture models like the Dirichlet process mixture model (DPMM) in that the number of latent events (i. e. clusters) is a random variable, but the point process formulation makes the NSP especially well suited to modeling spatiotemporal data.
Point process models for sequence detection in high-dimensional neural spike trains
Sparse sequences of neural spikes are posited to underlie aspects of working memory, motor production, and learning.
Fast Convolutive Nonnegative Matrix Factorization Through Coordinate and Block Coordinate Updates
Identifying recurring patterns in high-dimensional time series data is an important problem in many scientific domains.
A Provably Correct and Robust Algorithm for Convolutive Nonnegative Matrix Factorization
We present an algorithm that takes advantage of the NMF model underlying CNMF and exploits existing algorithms for separable NMF to provably find a solution under certain conditions.
