Neural-Koopman-Lyapunov-Control

Code for our paper: Neural Koopman Lyapunov Control

Requirements

The learning framework consists of the learner and falsifier (or verifier). The learner simultaneously learns the Control Lyapunov Function (CLF), Koopman based observables (encoder), decoder and the matrices constituting the Koopman bilinear system. The falsifier (SMT solver) is used to generate counterexamples that do no satisfy the Control Lyapunov conditions (or satisfy the falsification constraint). These counterexamples are added to the training set. The process is repeated till the SMT solver is not able to generate any counterexamples.

A typical process is as follows:

Define the neural network for the Control Lyapunov function (CLF), encoder (Koopman observables) and decoder.
Collect data from the unknown nonlinear system and add them to the training set
Set checking conditions for falsifier (falsification constraint)
Start training and verifying
Procedure stops when no counterexample is found by the SMT solver

If you face any issues installing the packages, please feel free to directly run the codes on Google Colab.

Neural Koopman Lyapunov Control

Related tags

Overview

Neural-Koopman-Lyapunov-Control

Requirements

A typical process is as follows:

Owner

Vrushabh Zinage

Code for our paper A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization,

Codes and scripts for "Explainable Semantic Space by Grounding Languageto Vision with Cross-Modal Contrastive Learning"

A simple, fully convolutional model for real-time instance segmentation.

Binary classification for arrythmia detection with ECG datasets.

Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.

Code for NeurIPS2021 submission "A Surrogate Objective Framework for Prediction+Programming with Soft Constraints"

Image transformations designed for Scene Text Recognition (STR) data augmentation. Published at ICCV 2021 Workshop on Interactive Labeling and Data Augmentation for Vision.

Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.

Using multidimensional LSTM neural networks to create a forecast for Bitcoin price

(CVPR 2021) Lifting 2D StyleGAN for 3D-Aware Face Generation

Deep Reinforcement Learning with pytorch & visdom

网络协议2天集训

Medical image analysis framework merging ANTsPy and deep learning

Expand human face editing via Global Direction of StyleCLIP, especially to maintain similarity during editing.

So-ViT: Mind Visual Tokens for Vision Transformer

TYolov5: A Temporal Yolov5 Detector Based on Quasi-Recurrent Neural Networks for Real-Time Handgun Detection in Video

Unsupervised clustering of high content screen samples

[CVPR'22] Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast

[ICCV 2021] Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation

A curated list of awesome Deep Learning tutorials, projects and communities.