Interpretable Deep Learning - Deep Learning in Life Sciences - Lecture 05 (Spring 2021)

Deep Learning in Life Sciences - Lecture 05 - Interpretable Deep Learning (Spring 2021)
6.874/6.802/20.390/20.490/HST.506 Spring 2021 Prof. Manolis Kellis
Deep Learning in the Life Sciences / Computational Systems Biology
Playlist: https://youtube.com/playlist?l....ist=PLypiXJdtIca5sxV
Latest slides and course today: http://compbio.mit.edu/6874
Spring 2021 slides and materials: http://mit6874.github.io/

0:00 Lecture outline
3:08 Interpretability: definition, importance
10:30 Interpretability: ante-hoc vs. post-hoc
18:26 Interpreting models: Weight visualization
22:20 Interpreting models: Surrogate model
24:14 Interpreting models: Activation Maximization / Data generation
34:26 Interpreting models: Example-based
39:36 Interpreting decisions
42:24 Interpreting decisions: Example based
45:39 Interpreting decisions: Attribution methods
1:01:17 Interpreting decisions: Gradient based
1:08:55 Interpreting decisions: Backprop-based
1:13:23 Evaluating attributions
1:14:15 Evaluating attributions: Coherence
1:15:30 Evaluating attributions: Class sensitivity
1:16:20 Evaluating attributions: Selectivity
1:19:45 Evaluating attributions: Remove and retrain/keep and retrain
1:21:15 Lecture summary