id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-143539-gvt25gac	Marmarelis, Myrl G.	Latent Embeddings of Point Process Excitations	2020-05-05		.txt	text/plain	5357	355	54	By performing synthetic experiments on short records as well as an investigation into options markets and pathogens, we demonstrate that learning the embedding alongside a point process model uncovers the coherent, rather than spurious, interactions. The propagation of disease [1] , news topics [2] , crime patterns [3, 4] , neuronal firings [5] , and market trade-level activity [6, 7] naturally suit the form of diachronic point processes with an underlying causal-interaction network. Furnished with the causality estimates in Eq. 6 (the "Expectation" step), we perform projected gradient ascent by setting partial derivatives of the complete-data log-likelihood with respect to each kernel parameter to zero (the "Maximization" step). We demonstrated the viability of estimating embeddings for events in an interpretable metric space tied to a self-exciting point process. The block point process model for continuous-time event-based dynamic networks Latent self-exciting point process model for spatial-temporal networks	./cache/cord-143539-gvt25gac.txt	./txt/cord-143539-gvt25gac.txt