Mykel Kochenderfer, Kunal Menda, Jean De Becdelièvre, Jayesh Gupta, Ilan Kroo, Zachary Manchester
We lifted 10 functions out of this paper's own repositories and ran 8 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| sisl/CEEM | canonical | 8 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| HarmonicDecayScheduler | Ran | sisl/CEEM/ceem/particleem.py code served (permissive licence) · get_code("b9784ff84cd8a51d") |
| compute_rms | Ran | sisl/CEEM/ceem/exp_utils.py code served (permissive licence) · get_code("68918bf29793f782") |
| ensure_default_torch_kwargs | Ran | sisl/CEEM/ceem/learner.py code served (permissive licence) · get_code("50f8cbf6de31a1af") |
| gen_ypred_benchmark | Ran | sisl/CEEM/ceem/exp_utils.py code served (permissive licence) · get_code("15c3ab8d13c9c4ae") |
| load_helidata | Ran | sisl/CEEM/ceem/data_utils.py code served (permissive licence) · get_code("b250895ef600807a") |
| load_statistics | Ran | sisl/CEEM/ceem/data_utils.py code served (permissive licence) · get_code("05ede9c21387428f") |
| make_output_format | Ran | sisl/CEEM/ceem/logger.py code served (permissive licence) · get_code("e6d5c8ead04a113a") |
| odestep | Ran | sisl/CEEM/ceem/odesolver.py code served (permissive licence) · get_code("acefc50eb4bbfcac") |
| filter_ | Not yet run | sisl/CEEM/ceem/nested.py code served (permissive licence) · get_code("333bf105fcfc9f91") |
| rk4_alt_step_func | Not yet run | sisl/CEEM/ceem/odesolver.py code served (permissive licence) · get_code("5a92e97beb204564") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
System identification is a key step for modelbased control, estimator design, and output prediction. This work considers the offline identification of partially observed nonlinear systems. We empirically show that the certainty-equivalent approximation to expectation-maximization can be a reliable and scalable approach for highdimensional deterministic systems, which are common in robotics. We formulate certaintyequivalent expectation-maximization as block coordinate-ascent, and provide an efficient implementation. The algorithm is tested on a simulated system of coupled Lorenz attractors, demonstrating its ability to identify high-dimensional systems that can be intractable for particle-based approaches. Our approach is also used to identify the dynamics of an aerobatic helicopter. By augmenting the state with unobserved fluid states, a model is learned that predicts the acceleration of the helicopter better than state-of-the-art approaches. The codebase for this work is available at https://github.com/sisl/CEEM.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2006.11615")
get_code_for_paper("2006.11615")
have("2006.11615")
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