Presentations

Note: to open the Keynote files, you will need to install the Computer Modern fonts. I use these fonts so that the main text of the slide matches the font of equations copied from TeX. If you do not install these fonts, the Keynote files will open but will have incorrect fonts so the layout of the text will be wrong.

Invited Talks

Adversarial Examples and Adversarial Training

• "The Case for Dynamic Defenses Against Adversarial Examples". ICLR SafeML Workshop, 2019. [slides(pdf)] [slides(key)]
• "Introduction to Adversarial Examples". KIBM Symposium on AI and the Brain. [slides(.key)]
• "Defense against the Dark Arts: An overview of adversarial example security research and future research directions". CVPR 2018 CV-COPS workshop. [slides(pdf)] [slides(key)]
• "Defense against the Dark Arts: An overview of adversarial example security research and future research directions". IEEE Deep Learning Security Workshop 2018. [slides+notes(pdf)] [slides(pdf)] [slides(key)]
• "Defending Against Adversarial Examples". NIPS 2017 Workshop on Machine Learning and Security. [slides(pdf)] [slides(key)]
• "Thermometer Encoding: One hot way to resist adversarial examples," 2017-11-15, Stanford University [slides(pdf)] [slides(key)]
• "Adversarial Examples and Adversarial Training," 2017-05-30, CS231n, Stanford University [slides(pdf)] [slides(key)]
• "Adversarial Examples and Adversarial Training," 2017-01-17, Security Seminar, Stanford University [slides(pdf)] [slides(key)]
• "Adversarial Examples and Adversarial Training," 2016-12-9, NIPS Workshop on Reliable ML in the Wild [slides(pdf)] [slides(key)] [video(wmv)]
• "Adversarial Examples and Adversarial Training," presentation at Uber, October 2016. [slides(pdf)]
• "Physical Adversarial Examples," presentation and live demo at GeekPwn 2016 with Alex Kurakan. [slides(pdf)]
• "Adversarial Examples and Adversarial Training," guest lecture for CS 294-131 at UC Berkeley. [slides(pdf)] [slides(key)] [video(youtube)]
• "Exploring vision-based security challenges for AI-driven scene understanding," joint presentation with Nicolas Papernot at AutoSens, September 2016, in Brussels. Access to the slides and video may be purchased at the conference website. They will be freely available after six months.
• "Adversarial Examples and Adversarial Training" at HORSE 2016. [slides(pdf)] [youtube]
• "Adversarial Examples and Adversarial Training" at San Francisco AI Meetup, 2016. [slides(pdf)]
• "Adversarial Examples and Adversarial Training" at Quora, Mountain View, 2016. [slides(pdf)]
• "Adversarial Examples" at the Montreal Deep Learning Summer School, 2015. [slides(pdf)] [video]
• "Do statistical models understand the world?" Big Tech Day, Munich, 2015. [youtube]
• "Adversarial Examples" Re-Work Deep Learning Summit, 2015. [youtube]

Generative Adversarial Networks

• "Introduction to GANs". CVPR 2018 Tutorial on GANs. [slides(pdf)]
• "Introduction to GANs". CVPR 2018 Workshop on Perception Beyond the Visible Spectrum. [slides(pdf)] [slides(.key)]
• "Overcoming Limited Data with GANs". NIPS 2017 Workshop on Limited Labeled Data. [slides(pdf)] [slides(key)]
• "Bridging theory and practice of GANs". NIPS 2017 Workshop on Bridging Theory and Practice of Deep Learning. [slides(pdf)] [slides(key)]
• "GANs for Creativity and Design". NIPS 2017 Workshop on Creativity and Design. [slides(pdf)] [slides(key)]
• "Giving artificial intelligence imagination using game theory". 35 under 35 talk at EmTech 2017. [slides(pdf)][slides(key)]
• "Generative Adversarial Networks". Introduction to ICCV Tutorial on Generative Adversarial Networks, 2017. [slides(pdf)] [slides(key)]
• "Generative Adversarial Networks". NVIDIA Distinguished Lecture Series, USC, September 2017. [slides(pdf)] [slides(key)]
• "Generative Adversarial Networks". Adobe Research Seminar, San Jose 2017. [slides(pdf)] [slides(keynote)]
• "Generative Adversarial Networks". GPU Technology Conference, San Jose 2017. [slides(pdf)] [slides(keynote)]
• "Generative Adversarial Networks". Re-Work Deep Learning Summit, San Francisco 2017. [slides(pdf)] [slides(keynote)]
• Panel discussion at the NIPS 2016 Workshop on Adversarial Training: Facebook video
• "Introduction to Generative Adversarial Networks," NIPS 2016 Workshop on Adversarial Training. [slides(keynote)] [slides(pdf)] [video (Facebook)]
• "Generative Adversarial Networks," NIPS 2016 tutorial. [slides(keynote)] [slides(pdf)] [video] [tech report(arxiv)]
• "Generative Adversarial Networks," a guest lecture for John Canny's COMPSCI 294 at UC Berkeley. Oct 2016. [slides(keynote)] [slides(pdf)] [youtube]
• "Generative Adversarial Networks" at AI With the Best (online conference), September 2016. [slides(pdf)]
• "Generative Adversarial Networks" keynote at MLSLP, September 2016, San Francisco. [slides]
• "Generative Adversarial Networks" at Berkeley AI Lab, August 2016. [slides(pdf)]
• "Generative Adversarial Networks" at NVIDIA GTC, April 2016. [slides(pdf)][video]
• "Generative Adversarial Networks" at ICML Deep Learning Workshop, Lille, 2015. [slides(pdf)] [video]
• "Generative Adversarial Networks" at NIPS Workshop on Perturbation, Optimization, and Statistics, Montreal, 2014. [slides(pdf)]

Other Subjects

• "Adversarial Machine Learning". ICLR Keynote, 2019. [slides(pdf)] [slides(key)]
• "Adversarial Machine Learning". AAAI Plenary Keynote, 2019. [slides(pdf)] [slides(kes)]
• "Adversarial Machine Learning". ACM Webinar, 2018. [slides(pdf)] [slides(key)]
• "Adversarial Machine Learning". South Park Commons, 2018. [slides(pdf)] [slides(key)]
• "Security and Privacy of Machine Learning". RSA 2018. [slides(pdf)] [slides(key)]
• "Adversarial Robustness for Aligned AI". NIPS 2017 Workshop on Aligned AI. [slides(pdf)] [slides(key)]
• "Defense Against the Dark Arts: Machine Learning Security and Privacy," BayLearn, 2017-10-19. [slides(pdf)] [video(youtube)]
• "Adversarial Machine Learning for Security and Privacy," Army Research Organization workshop, Stanford, 2017-09-14. [slides(pdf)]
• "Generative Models I," 2017-06-27, MILA Deep Learning Summer School. [slides(pdf)] [slides(key)]
• "Adversarial Approaches to Bayesian Learning and Bayesian Approaches to Adversarial Robustness," 2016-12-10, NIPS Workshop on Bayesian Deep Learning [slides(pdf)] [slides(key)]
• "Design Philosophy of Optimization for Deep Learning" at Stanford CS department, March 2016. [slides(pdf)]
• "Tutorial on Optimization for Deep Networks" Re-Work Deep Learning Summit, 2016. [slides(keynote)] [slides(pdf)]
• "Tutorial on Neural Network Optimization Problems" at the Montreal Deep Learning Summer School, 2015. [slides(pdf)] [video]
• "Practical Methodology for Deploying Machine Learning" Learn AI With the Best, 2015. [slides(pdf)] [youtube]

Contributed Talks

• "Qualitatively characterizing neural network optimization problems" at ICLR 2015. [slides(pdf)]
• "Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks" with Yaroslav Bulatov and Julian Ibarz at ICLR 2014. [youtube]
• "Maxout Networks" at ICML 2013. [video]
• "Joint Training Deep Boltzmann Machines for Classification" at ICLR 2013 (workshop track). [video]

Miscellaneous

• I've made several presentations for the Deep Learning textbook, and presented some of them at a study group for the book.
• PhD thesis defense. [youtube] [slides]