csjtx1021
I am an associate professor at Jilin University. My research interests include Bayesian Optimization, Graph Generation, and Network Dynamics Learning.
Jilin UniversityChina
Pinned Repositories
CAGG
Cost-Aware Graph Generation (CAGG), a framework for generating graphs with the optimal properties at as low cost as possible. The work has been accepted by AAAI 2021. (Python3/Pytorch)
collect_preference_data_tool
This tool is used to collect the human preference data of odor molecules. (Python3)
csjtx1021.github.io
This is Jiaxu Cui's homepage
DGBO
This code is implemented according to paper "Deep Bayesian Optimization on Attributed Graphs", published on AAAI2019. (Python2/TensorFlow)
neural_ode_processes_for_network_dynamics-master
Neural ODE Processes for Network Dynamics (NDP4ND), a new class of stochastic processes governed by stochastic data-adaptive network dynamics, is to overcome the fundamental challenge of learning accurate network dynamics with sparse, irregularly-sampled, partial, and noisy observations.
Scalable-and-Parallel-DGBO
This code is implemented according to paper "Scalable and Parallel Deep Bayesian Optimization on Attributed Graphs", accepted by TNNLS. (Python2/TensorFlow)
Spatio-Temporal-
Matlab codes used in the paper "Predicting the patterns of spatio-temporal signal propagation in complex networks"
PySR
High-Performance Symbolic Regression in Python and Julia
csjtx1021's Repositories
csjtx1021/CAGG
Cost-Aware Graph Generation (CAGG), a framework for generating graphs with the optimal properties at as low cost as possible. The work has been accepted by AAAI 2021. (Python3/Pytorch)
csjtx1021/neural_ode_processes_for_network_dynamics-master
Neural ODE Processes for Network Dynamics (NDP4ND), a new class of stochastic processes governed by stochastic data-adaptive network dynamics, is to overcome the fundamental challenge of learning accurate network dynamics with sparse, irregularly-sampled, partial, and noisy observations.
csjtx1021/Scalable-and-Parallel-DGBO
This code is implemented according to paper "Scalable and Parallel Deep Bayesian Optimization on Attributed Graphs", accepted by TNNLS. (Python2/TensorFlow)
csjtx1021/DGBO
This code is implemented according to paper "Deep Bayesian Optimization on Attributed Graphs", published on AAAI2019. (Python2/TensorFlow)
csjtx1021/collect_preference_data_tool
This tool is used to collect the human preference data of odor molecules. (Python3)
csjtx1021/csjtx1021.github.io
This is Jiaxu Cui's homepage
csjtx1021/Spatio-Temporal-
Matlab codes used in the paper "Predicting the patterns of spatio-temporal signal propagation in complex networks"