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Date & Time: Tuesday, 12 February,
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Classroom Contents
Toward Theoretical Understanding of Deep Learning - Lecture 2
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- 1 Date & Time: Tuesday, 12 February,
- 2 Date & Time: Tuesday, 12 February,
- 3 Date & Time: Wednesday, 13 February,
- 4 Start
- 5 Toward theoretical understanding of deep learning
- 6 Machine learning ML: A new kind of science
- 7 Recap:
- 8 Training via Gradient Descent "natural algorithm"
- 9 Subcase: deep learning*
- 10 Brief history: networks of "artificial neurons"
- 11 Some questions
- 12 Part 1: Why overparameterization and/or overprovisioning?
- 13 Overprovisioning may help optimization part 1: a folklore experiment
- 14 Overprovisioning can help part 2: Allowing more
- 15 Acceleration effect of increasing depth
- 16 But textbooks warn us: Larger models can "Overfit"
- 17 Popular belief/conjecture
- 18 Noise stability: understanding one layer no nonlinearity
- 19 Proof sketch : Noise stability -deep net can be made low-dimensional
- 20 The Quantitative Bound
- 21 Correlation with Generalization qualitative check
- 22 Concluding thoughts on generalization
- 23 Part 2: Optimization in deep learning
- 24 Basic concepts
- 25 Curse of dimensionality
- 26 Gradient descent in unknown landscape.
- 27 Gradient descent in unknown landscape contd.
- 28 Evading saddle points..
- 29 Active area: Landscape Analysis
- 30 New trend: Trajectory Analysis
- 31 Trajectory Analysis contd
- 32 Unsupervised learning motivation: "Manifold assumption"
- 33 Unsupervised learning Motivation: "Manifold assumption" contd
- 34 Deep generative models
- 35 Generative Adversarial Nets GANs [Goodfellow et al. 2014]
- 36 What spoils a GANs trainer's day: Mode Collapse
- 37 Empirically detecting mode collapse Birthday Paradox Test
- 38 Estimated support size from well-known GANs
- 39 To wrap up....What to work on suggestions for theorists
- 40 Concluding thoughts
- 41 Advertisements
- 42 Q&A
