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About this paper
Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron Courville, Mehdi Mirza, Ben Hamner, Will Cukierski, Yichuan Tang and 20 more
The authors examine what machine-learning contests reveal about methods chosen by practitioners. They report that sparse filtering helped a winning black-box entry score 70.22%, while convolutional networks led facial-expression recognition with 71.162%, near a hand-engineered approach at 67.484%. A multimodal contest produced 100% through global matching, not demonstrated semantic learning; scores are evidence to investigate, not controlled proof of superiority.
Summary by alphaXiv.
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