ARCH is a hierarchical flow-based generative model that enables tractable conditional intensity computation and arbitrary conditioning for spatiotemporal event distributions.
arXiv preprint arXiv:1807.11880 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
SNMPP builds a product-form neural influence kernel from a signed interaction network over event classes and a delay-aware monotonic temporal network to enable explicit discovery of inter-event relationships alongside strong prediction.
Proposes a constrained stochastic compositional gradient descent algorithm for queuing system design formulated as compositional stochastic programs, along with non-asymptotic iteration complexity bounds.
citing papers explorer
-
Arbitrarily Conditioned Hierarchical Flows for Spatiotemporal Events
ARCH is a hierarchical flow-based generative model that enables tractable conditional intensity computation and arbitrary conditioning for spatiotemporal event distributions.
-
Structured Neural Marked Point Processes for Interpretable Event Interaction Modeling
SNMPP builds a product-form neural influence kernel from a signed interaction network over event classes and a delay-aware monotonic temporal network to enable explicit discovery of inter-event relationships alongside strong prediction.
-
Optimal Design of Queuing Systems via Compositional Stochastic Programming
Proposes a constrained stochastic compositional gradient descent algorithm for queuing system design formulated as compositional stochastic programs, along with non-asymptotic iteration complexity bounds.