Wow, I never heard of doing annual mammograms, or even "base-line screening" (!). Wouldn't that actually count as personal injury?
EDIT: Continued to read the actual article -- I didn't know about the mammography madness in the US. AFAIK doing X-Rays or CTs without reasonable suspicion is considered personal injury here (in Germany).
All that being said, anyone care to weigh in on the pros/cons of RBMs vs something like a contractive autoencoder?
For some problems, it may be nice to have a generative model as offered by RBMS (although Rifai et al. published a sampling method for contractive auto-encoders recently: http://icml.cc/2012/papers/910.pdf). I feel like with RBMs, you can design models which incorporate prior knowledge more "easily" (you may end up with pretty complex models...), e.g. the conditional RBM, the mean-covariance RBM or the spike & slab RBM. Additionally, there's the deep boltzmann machine that consists of multiple layers that are jointly trained in an RBM-like fashion.
Auto-encoders are straightforward to understand and implement. With contractive terms or denoising, the are powerful feature extractors as well.
But as you already noted, if you "just" want to have a good classifier, I think it pretty much boils down to personal preference since you're going to spend some effort on making these techniques work well on your problem anyway.
While looking cool, it seems to me like this catches a pedestrian's attention more than the one of the drivers. If pedestrians are distracted from traffic right before crossing, this would make crosswalks less safe.
It should be more useful (but more expensive) to lighten up the actual crosswalk signs on the road as soon as pedestrians are approaching.
Cool, I just found out it works for beamer, too. Having instant preview for presentations is great, as I often find myself moving figures around all the time.
