I-ROCKET: Interpretable ROCKET

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Authors: Mark Laubach
Cited by: 0

Abstract

0.2.0 (2026-03-18) Feature stability selection: Added get_stable_features() as a robust alternative to RFE for identifying important features across CV folds. Class-mean visualization functions: Added plot_class_mean_activation() (side-by-side activation and convolution output), plot_multi_kernel_summary() (heatmap across classes), and plot_aggregate_activation() (importance-weighted sum with differential). Aggregate temporal occlusion: Added aggregate_temporal_occlusion() for computing occlusion sensitivity over all trials grouped by class. RF diagram font size: Increased y-axis label font size from 5.5 to 8 for readability. Public kneedle: Renamed _kneedle to kneedle (public API). CI fix: Corrected tests.yml to use pip install -e . (removed nonexistent [viz] extra). pyproject.toml: Added three_bumps to py-modules. Demonstration notebooks for Receptive Field Mapping (demo_RF_mapping.ipynb) and the Breiman waveform dataset (demo_waveform.ipynb) and two benchmark scripts (benchmark_waveform.py and benchmark_ucr.py) were added to the examples directory.

Fields of Study & Subjects

Explainable Artificial Intelligence (XAI) Time Series Analysis and Forecasting Neural Networks and Applications

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
16 March 2026
Language
EN