18 citations · 22 across the 3 of their papers we have counts for
4 papers · 1 filter
Cardinality-Regularized Hawkes-Granger Model
Tsuyoshi Idé, Georgios Kollias, Dzung T. Phan +1
We propose a new sparse Granger-causal learning framework for temporal event data. We focus on a specific class of point processes called the Hawkes process. We begin by pointing o…
Directed Graph Auto-Encoders
Georgios Kollias, Vasileios Kalantzis, Tsuyoshi Idé +2
We introduce a new class of auto-encoders for directed graphs, motivated by a direct extension of the Weisfeiler-Leman algorithm to pairs of node labels. The proposed model learns…
Accelerating Physics-Based Simulations Using Neural Network Proxies: An Application in Oil Reservoir Modeling
Jiri Navratil, Alan King, Jesus Rios +3
We develop a proxy model based on deep learning methods to accelerate the simulations of oil reservoirs--by three orders of magnitude--compared to industry-strength physics-based P…
Provably convergent acceleration in factored gradient descent with applications in matrix sensing
Tayo Ajayi, David Mildebrath, Anastasios Kyrillidis +3
We present theoretical results on the convergence of \emph{non-convex} accelerated gradient descent in matrix factorization models with -norm loss. The purpose of this work…