Breakthrough In Automatic Handwritten Character Recognition Sans Deep Learning (technologyreview.com)

subh_arya writes: Researchers from NYU, UToronto and MIT have come up with a technique that captures human learning abilities for a large class of simple visual concepts to recognize handwritten characters from World's Alphabet. Their computational model (abstract) represents concepts as simple programs that best explain observed examples under a Bayesian criterion. Unlike recent deep learning approaches that require thousands of examples to train an efficient model, their model can achieve human-level performance with only one example. Additionally, the authors present several "visual Turing tests" probing the model's creative generalization abilities, which in many cases are indistinguishable from human behavior.

Comments

Oblig link: source code

[Anonymous Coward]

If only the /. editors would do some minimal investigation... Oh wait, this is still /.

https://github.com/brendenlake/BPL