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?list=PLypiXJdtIca5sxV7aE3-PS9fYX3vUdIOX
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
Видео Interpretable Deep Learning - Deep Learning in Life Sciences - Lecture 05 (Spring 2021) канала Manolis Kellis
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?list=PLypiXJdtIca5sxV7aE3-PS9fYX3vUdIOX
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
Видео Interpretable Deep Learning - Deep Learning in Life Sciences - Lecture 05 (Spring 2021) канала Manolis Kellis
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Deep Learning: A Crash Course
Lecture 12 | Visualizing and Understanding
Deep Learning in Life Sciences - Lecture 01 - Course Intro, AI, ML (Spring 2021)
1. Introduction for 15.S12 Blockchain and Money, Fall 2018
But what is a Neural Network? | Deep learning, chapter 1
Single Cell Genomics - Lecture 10 - Deep Learning in Life Sciences (Spring 2021)
Manolis Kellis at Athens Science Festival 2021 - Dissecting Disease Circuitry
Manolis Kellis - Darwin Day - Purchase University - February 9, 2021
Deep Learning 1: Introduction to Machine Learning Based AI
1. Introduction to Human Behavioral Biology
Manolis Kellis on Single-Cell Dissection of Disease Circuitry on January 21, 2021
CNNs Convolutional Neural Networks - Deep Learning in Life Sciences - Lecture 03 (Spring 2021)
LECTURE: Introduction to Epithelial & Connective Tissues
Deep Learning for Regulatory Genomics - Regulator binding, Transcription Factors TFs
Visualizing and Understanding Deep Neural Networks by Matt Zeiler
Quantum Computing for Computer Scientists
Session 1 - Introduction to Bioinformatics
MIT CompBio Lecture 01 - Introduction
Einstein's General Theory of Relativity | Lecture 1
Recurrent Neural Networks RNNs, Graph Neural Networks GNNs, Long Short Term Memory LSTMs