4 citations · 4 across the 2 of their papers we have counts for
3 papers
eess.SP2020
Dynamic Graph Learning based on Graph Laplacian
Bo Jiang, Ashkan Panahi, Hamid Krim +2
The purpose of this paper is to infer a global (collective) model of time-varying responses of a set of nodes as a dynamic graph, where the individual time series are respectively…
cs.LG2019★ 4 cited
Deep Adversarial Belief Networks
Yuming Huang, Ashkan Panahi, Hamid Krim +2
We present a novel adversarial framework for training deep belief networks (DBNs), which includes replacing the generator network in the methodology of generative adversarial netwo…
stat.ML2019
Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations
Stephen L. Keeley, David M. Zoltowski, Yiyi Yu +3
Gaussian Process Factor Analysis (GPFA) has been broadly applied to the problem of identifying smooth, low-dimensional temporal structure underlying large-scale neural recordings.…