SUMS 2020: Bridging the Gap Between Computers and Semantics Through Learned Information Representations

Max Daniels
Northeastern University
[email protected]

Thanks for listening to the talk!

Here is the associated work: Statistical Distances and Their Implications to GAN Training

You can download the slides here.

Edit, Mar. 8: For those who are interested, here are some especially interesting resources related to the talk:
  1. Unsupervised Representation Learning:
    1. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric argues that extraction of semantic features is a general emergent phenomena of deep visual representations. (+1 for Magic of Deep Learning)
    2. Image Classfiers are biased towards textures over shapes argues that deep image classifiers are biased towards texture extraction. (-1 for Magic of Deep Learning)
  2. Word2Vec:
  3. GAN Training:
  4. Compressive Sensing: this is what I spend most of my actual time on. Learn more about image compression, JPEG, MRI machines, and signal processing!