SimTech Project PN6-3 "Understanding Physical Constraints in Machine Learning for Simulation"
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computation.py
Mar 23, 2021 - Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression
Python Source Code - 16.2 KB - MD5: 727ea9050972d03f12c6aa5b32b7e37c
custom_paths.py
Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent
Python Source Code - 58 B - MD5: 2b8520afbcb24f93040273f4edfee4b7
data.tar.gz
Mar 23, 2021 - Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression
Gzip Archive - 5.6 GB - MD5: 220970917c2371206a808c02bbb9f359
Data
Data generated by running the code
eval_nn_setups.py
Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent
Python Source Code - 19.2 KB - MD5: 06303383461187b4df57f1e6a54afb12
eval_star_dataset.py
Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent
Python Source Code - 2.0 KB - MD5: 49e899167f27aa3a56082814c8446a2a
LICENSE
Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent
Plain Text - 11.1 KB - MD5: 620a9251fad92ed98289fc61c7ee3681
main.py
Mar 23, 2021 - Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression
Python Source Code - 2.7 KB - MD5: d298130fc29f0a84b33b58ad0d100a06
mc_event_estimation.py
Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent
Python Source Code - 4.7 KB - MD5: 81fe6dd140e7b4020884a3af4f310285
mc_plotting.py
Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent
Python Source Code - 5.9 KB - MD5: a945f74f987c58d0cd7e8f5410390a11
mc_sgd_keras.py
Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent
Python Source Code - 8.7 KB - MD5: 22e33f1b8fb56e9bce0f56ad0386afea
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