6 citations · 10 across the 8 of their papers we have counts for
5 papers · 1 filter
Learning Temporal Causal Structure via Smooth Differentiable Optimization
Tong Zhao, Ce Guo, Wayne Luk +2
Causal discovery with instantaneous effects in multivariate time series is challenging, as the instantaneous structure must be acyclic. Prior methods enforce this by either separat…
Robust Time Series Causal Discovery for Agent-Based Model Validation
Gene Yu, Ce Guo, Wayne Luk
Agent-Based Model (ABM) validation is crucial as it helps ensuring the reliability of simulations, and causal discovery has become a powerful tool in this context. However, current…
Scalable Time-Series Causal Discovery with Approximate Causal Ordering
Ziyang Jiao, Ce Guo, Wayne Luk
Causal discovery in time-series data presents a significant computational challenge. Standard algorithms are often prohibitively expensive for datasets with many variables or sampl…
An Analysis of Alternating Direction Method of Multipliers for Feed-forward Neural Networks
Seyedeh Niusha Alavi Foumani, Ce Guo, Wayne Luk
In this work, we present a hardware compatible neural network training algorithm in which we used alternating direction method of multipliers (ADMM) and iterative least-square meth…
An FPGA Accelerated Method for Training Feed-forward Neural Networks Using Alternating Direction Method of Multipliers and LSMR
Seyedeh Niusha Alavi Foumani, Ce Guo, Wayne Luk
In this project, we have successfully designed, implemented, deployed and tested a novel FPGA accelerated algorithm for neural network training. The algorithm itself was developed…