At snowbird, the talk on Sum-Product networks by Hoifung Poon stood out to me (Pedro Domingos is a coauthor.). The basic point was that by appropriately constructing networks based on sums and products, the normalization problem in probabilistic models is eliminated, yielding a highly tractable yet flexible representation+learning algorithm. As an algorithm, this is noticeably cleaner than deep belief networks with a claim to being an order of magnitude faster and working better on an image completion task.
Snowbird doesn’t have real papers—just the abstract above. I look forward to seeing the paper. (added: Rodrigo points out the deep learning workshop draft.)