Reading Assignment: Introspection & Advanced Architectures

Optional: Manifolds

If you would like to follow up on the concept of manifolds, read the blog post on “Neural Networks, Manifolds, and Topology” by Christopher Olah.

Main Reading

Start with the high-level overview provided in this blog post by Adit Deshpande. Anything beyond the section on ResNets is optional. (GANs are covered in the “Learning Generative Models” course.)

Next, read this overview blog post on feature visualization.

Now it’s time to look at some specific techniques. Dig as deep as you like! These websites provide plenty of background information.

1. Deepvis (code and paper are linked, optional further reading on fooling neural nets)

2. LSTMVis (code and paper are linked)

3. Heatmapping - especially Methods for Interpreting and Understanding Deep Neural Networks (2017), Montavon et al.

Further Reading

• for the course project (Only shallow reading required - focus on the architecture!): Wav2Letter: an End-to-End ConvNet-based Speech Recognition System (2016), Collobert et al.
• (optional) on ResNets: Deep residual learning for image recognition (2016), He et al.
• (optional) on Inception architectures: Rethinking the inception architecture for computer vision (2016), Szegedy et al.