Basic16- Basic Deep NN and Robustness

Presenter	Papers	Paper URL	Our Slides
AE	Intriguing properties of neural networks /	PDF
AE	Explaining and Harnessing Adversarial Examples	PDF
AE	Towards Deep Learning Models Resistant to Adversarial Attacks	PDF
AE	DeepFool: a simple and accurate method to fool deep neural networks	PDF
AE	Towards Evaluating the Robustness of Neural Networks by Carlini and Wagner	PDF	PDF
Data	Basic Survey of ImageNet - LSVRC competition	URL	PDF
Understand	Understanding Black-box Predictions via Influence Functions	PDF
Understand	Deep inside convolutional networks: Visualising image classification models and saliency maps	PDF
Understand	BeenKim, Interpretable Machine Learning, ICML17 Tutorial [^1]	PDF
provable	Provable defenses against adversarial examples via the convex outer adversarial polytope, Eric Wong, J. Zico Kolter,	URL

[^1] Notes about Interpretable Machine Learning

Notes of Interpretability in Machine Learning from Been Kim Tutorial

by Brandon Liu

Important Criteria in ML Systems

• Safety
• Nondiscrimination
• Avoiding technical debt
• Providing the right to explanation
• Ex. Self driving cars and other autonomous vehicles - almost impossible to come up with all possible unit tests.

What is interpretability?

• The ability to give explanations to humans.

Two Branches of Interpretability

• In the context of an application: if the system is useful in either a practical application or a simplified version of it, then it must be somehow interpretable.
• Via a quantifiable proxy: a researcher might first claim that some model class—e.g. sparse linear models, rule lists, gradient boosted trees—are interpretable and then present algorithms to optimize within that class.

Before building any model

• Visualization
• Exploratory data analysis

Building a new model

• Rule-based, per-feature-based
• Case-based
• Sparsity
• Monotonicity

After building a model

• Sensitivity analysis, gradient-based methods
• mimic/surrogate models
• Investigation on hidden layers