ARCH is a hierarchical flow-based generative model that enables tractable conditional intensity computation and arbitrary conditioning for spatiotemporal event distributions.
Stochastic gradient descent with b iased but consistent gradient estimators
3 Pith papers cite this work. Polarity classification is still indexing.
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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
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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.
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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.
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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.