Cui Jiaxu's starred repositories
torchdiffeq
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
learning-to-learn
Learning to Learn in TensorFlow
pytorch_geometric_temporal
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
EEG-Datasets
A list of all public EEG-datasets
set_transformer
Pytorch implementation of set transformer
Meta-Learning-Papers
A classified list of meta learning papers based on realm.
logictensornetworks
Deep Learning and Logical Reasoning from Data and Knowledge
Neural-Process-Family
Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.
sympytorch
Turning SymPy expressions into PyTorch modules.
fast-symbolic-regression
Blazing fast symbolic regresison
NeuralSymbolicRegressionThatScales
Source code and Dataset creation for the paper "Neural Symbolic Regression That Scales"
NeuralProcesses.jl
A framework for composing Neural Processes in Julia
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.
dynamic-system
Simulation of systems described by differential equations
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)
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
Transformer-based-Symbolic-Regression
A faster implementation version of the paper "Neural Symbolic Regression that scales"