Structured framework for learning mechanical systems in PyTorch
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Updated
Apr 15, 2019 - Python
Structured framework for learning mechanical systems in PyTorch
ASSESS is a passive model learning method for IoT device, that infers a system of LTSs (Labelled Transition Systems) from execution traces. Each LTS of the system will represent a different component of the device.
The provided program robustly learns a multilinear face model from databases with missing data, corrupt data, wrong semantic correspondence, and inaccurate vertex correspondence.
The provided program jointly optimizes a multilinear face model and the registration of the face scans used for model training.
Matlab implementation of online and window dynamic mode decomposition algorithms
Incremental Sparse Spectrum Gaussian Process Regression
Code for the paper Data-efficient model learning and prediction for contact-rich manipulation tasks, RA-L, 2020
🔨 A prototype tool for learning DOTAs based on mutation testing.
🔨 A prototype tool for learning DOTAs based on PAC.
🔨 A prototype tool for learning DOTAs exactly.
🔧 A prototype tool on learning real-time automata based on pac.
🏆 时间自动机模型学习工具站点(Timed Automata)
Code for the paper Data-efficient model learning and prediction for contact-rich manipulation tasks, RA-L, 2020
AI4Science: Efficient data-driven Online Model Learning (OML) / system identification and control
AI4Science: Python/Matlab implementation of online and window dynamic mode decomposition (Online DMD and Window DMD)
Grammatical inference using the Z3 SMT solver
Official implementation of L4DC 2023 paper Transition Occupancy Matching -Learning Policy-Aware Models for Model-Based Reinforcement Learning via Transition Occupancy Matching
Visualization of survey data.
Performance-oriented model learning for control via multi-objective Bayesian optimization
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