**Invited Talk:** “A Probabilistic Framework for Deep Learning: Understanding Convnets and Moving Beyond.” *University of California Berkeley*, Spring 2018.

**Invited Talk:** “A Probabilistic Framework for Deep Learning: Understanding Convnets and Moving Beyond.” *Stanford University*, October 2017.

“A Probabilistic Framework for Deep Learning: Understanding Convnets and Moving Beyond.” *Google Cloud AI Group & Amazon Research*, October 2017.

**Invited Talk:** “A Probabilistic Framework for Deep Learning: Understanding Convnets and Moving Beyond.” Center for Theoretical Neuroscience, Columbia University. NYC, NY, February 17, 2017.

“A Probabilistic Framework for Deep Learning: Understanding Convnets and Moving Beyond.” Seminar Series, Simons Institute. NYC, NY, February 16, 2017.

**Invited Talk: **“Beyond Convnets: The Next-Generation of Highly Scalable Architectures and Unsupervised Learning Algorithms.” *Google Seminar Series on Deep Learning.* Mountain View, California, August 18, 2016.

“A Probabilistic Theory of Deep Learning: How and Why Deep Convnets Work.” In Information Theory & Applications. San Diego, California, February 5, 2016.

“A Probabilistic Theory of Deep Learning: Or How I Learned to Love Neural Nets.” NIPS Workshop on Multi-scale Learning. Montreal Canada, December 2015. (Due to sickness, talk was given by Richard G. Baraniuk instead).

**Invited Talk:** “A Probabilistic Theory of Deep Learning: Applications to Computational Neuroscience.” CBCL Seminar, Tomaso Poggio Lab, Brain and Cognitive Science Dept., MIT. October 2015.

“How and Why Deep Learning Works: Applications to Computational Neuroscience.” Jim DiCarlo Lab, Brain and Cognitive Science Dept., MIT. October 2015.

**Invited Talk:** “How and Why Deep Learning Works” ISS Seminar Series, SEAS Dept., Harvard University. October 2015.

**Invited Talk:** “A Tutorial on Deep Learning: Why Does it Work?” International Conference of Computational Photography. Held at Rice University. April 25, 2015.