One of my favorite samples from the Progressive GANs paper is this one from the "cat" category. Apparently some of the cat training photos were memes with text. The GAN doesn't know what text is so it has made up new text-like imagery in the right place for a meme caption.
I never heard back from MIT. I got rejected from CMU. I was accepted to U of T but not to work with the PI I wanted there. I got "honorable mention" for NSF GRFP but not actual money. Don't let temporary failures discourage you.
We’re announcing a research collaboration with @CFS_energy, one of the world’s leading nuclear fusion companies.
Together, we’re helping speed up the development of clean, safe, limitless fusion power with AI. ⚛️
When Schmidhuber interrupted my talk on GANs in 2016, he did so at the time I was giving credit to relevant prior work: noise contrastive estimation. Schmidhuber isn’t about proper about academic credit assignment, he’s about self-aggrandizement.
ML researchers, reviewers, and press coverage of ML need to get a lot more serious about statistically robustness of results and the effect of hyperparameters. This study shows that many papers over the last year or so were just observing sampling error, not true improvement.
Posting a call for help: does anyone know of a good way to simultaneously treat both POTS and Ménière’s disease? Please contact me if you’re either a clinician with experience doing this or a patient who has found a good solution. Context in thread
By looking at this image, you can see how sensitive your own eyes are to contrast at different frequencies (taller apparent peaks=more sensitivity at that frequency). It's like a graph that is made by perceiving the graph itself. h/t @catherineolsfourier.eng.hmc.edu/e180/lectures/…