Tyler Soderstrom, Angan Mukherjee, Michael Kurtz, Victor Zavala
We lifted 10 functions out of this paper's own repositories and ran 9 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 |
|---|---|---|
| zavalab/ML | canonical | 9 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| biomarker_data | Ran | zavalab/ML/Asthma/src/result_utils.py pointer only (licence: NONE) · get_code("a9124c8aaaf659e9") |
| compute_gradients | Ran | zavalab/ML/Asthma/src/saliency.py pointer only (licence: NONE) · get_code("94132dc246945836") |
| get_cnn_result | Ran | zavalab/ML/Asthma/src/result_utils.py pointer only (licence: NONE) · get_code("91a4a9e4f671fe99") |
| get_data | Ran | zavalab/ML/Asthma/src/train_cnn.py pointer only (licence: NONE) · get_code("523cf7d200ff0e50") |
| get_metrics | Ran | zavalab/ML/Asthma/src/result_utils.py pointer only (licence: NONE) · get_code("7b0ae182cdf4004f") |
| integral_approximation | Ran | zavalab/ML/Asthma/src/saliency.py pointer only (licence: NONE) · get_code("b23a1eeb1fce1111") |
| interpolate_data | Ran | zavalab/ML/Asthma/src/saliency.py pointer only (licence: NONE) · get_code("0c8f6b39addeea0b") |
| nll | Ran | zavalab/ML/AUTOGNNUQ/gnn_uq/gnn_model.py pointer only (licence: NONE) · get_code("375315f53b291d52") |
| reduce | Ran | zavalab/ML/CMC_GCN/code/model_GNN.py pointer only (licence: NONE) · get_code("0d5fd0c9a54335f2") |
| list_string_to_list | Not yet run | zavalab/ML/Asthma/src/aux_utils.py pointer only (licence: NONE) · get_code("c4c7865f3905bf67") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Real-time process monitoring requires methods that extract actionable information from highdimensional time-series data. In this work, we present a new approach for process monitoring that combines tools of topological data analysis (TDA) and machine learning. In the proposed approach, we represent multivariate time-series data as manifolds and use topological descriptors to summarize the structure of such data; we then use a neural ordinary differential equation to learn the dynamic evolution of the topological structure of the system. Using real data from an industrial process, we show that this trajectory-based event detection approach is effective at detecting diverse types of events. We contrast this approach against reconstruction-based approaches such as principal component analysis and autoencoders and against a trajectory-based approach that uses Koopman autoencoders.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2606.20443")
get_code_for_paper("2606.20443")
have("2606.20443")
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