csjtx1021
I am an associate professor at Jilin University. My research interests include Bayesian Optimization, Graph Generation, and Network Dynamics Learning.
Jilin UniversityChina
csjtx1021's Stars
CSSEGISandData/COVID-19
Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE
openai/CLIP
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
521xueweihan/GitHub520
:kissing_heart: 让你“爱”上 GitHub,解决访问时图裂、加载慢的问题。(无需安装)
rtqichen/torchdiffeq
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
google-deepmind/learning-to-learn
Learning to Learn in TensorFlow
rawpython/remi
Python REMote Interface library. Platform independent. In about 100 Kbytes, perfect for your diet.
huawei-noah/HEBO
Bayesian optimisation & Reinforcement Learning library developped by Huawei Noah's Ark Lab
benedekrozemberczki/pytorch_geometric_temporal
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
meagmohit/EEG-Datasets
A list of all public EEG-datasets
SheffieldML/GPy
Gaussian processes framework in python
google-research/torchsde
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
dynamicslab/pysindy
A package for the sparse identification of nonlinear dynamical systems from data
juho-lee/set_transformer
Pytorch implementation of set transformer
oneHuster/Meta-Learning-Papers
A classified list of meta learning papers based on realm.
logictensornetworks/logictensornetworks
Deep Learning and Logical Reasoning from Data and Knowledge
SheffieldML/PyDeepGP
Deep Gaussian Processes in Python
YannDubs/Neural-Process-Family
Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.
cfusting/fast-symbolic-regression
Blazing fast symbolic regresison
wesselb/NeuralProcesses.jl
A framework for composing Neural Processes in Julia
crisbodnar/ndp
Official code for the ICLR 2021 paper Neural ODE Processes
b4silio/MLDemos
Machine Learning Demonstrations: A graphical interface to draw data, apply a diverse array of machine learning tools to it, and directly see the results in a visual and understandable manner.
misokg/NIPS2017
Multi-Information Source Optimization
pySRURGS/pySRURGS
Symbolic regression by uniform random global search
cfusting/fastgp
Fast Genetic Programming
shib0li/BMBO-DARN
Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks
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.
andars/dynamic-system
Simulation of systems described by differential equations
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)
hanyh0807/learning_to_learn_without_gd_by_gd
Naive implementation of Learning to Learn without Gradient Descent by Gradient Descent, Yutian Chen et al., ICML 2017