June 2014 Archives

Hello, and welcome to Paper of the Day (Po'D): Intriguing properties of neural networks edition. Today's paper is: C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow and R. Fergus, "Intriguing properties of neural networks", in Proc. Int. Conf. Learning Representations, 2014. Today's paper is very exciting for me because I see "horses" nearly being called "horses" in a machine learning research domain outside music information retrieval. Furthermore, the arguments that this work is apparently causing resembles what I have received in peer review of my work. For instance, see the comments on this post. Or the reviews here. Some amount of press is also resulting, e.g., ZDnet, Slashdot; and the results of the paper are also being used to bolster the argument that the hottest topic in machine learning is over-hyped.

The one-line precis of this paper is: The deep neural network: as uninterpretable as it ever was; and now acting in ways the contradict notions of generalization.

Hello, and welcome to Paper of the Day (Po'D): Horses and more horeses edition. Today's paper is: B. L. Sturm, "A Simple Method to Determine if a Music Information Retrieval System is a `Horse'", IEEE Trans. Multimedia, 2014 (in press). This double-header of a Po'D also includes this paper: B. L. Sturm, C. Kereliuk, and A. Pikrakis, "A Closer Look at Deep Learning Neural Networks with Low-level Spectral Periodicity Features", Proc. 4th International Workshop on Cognitive Information Processing, June 2014.

The one-line precis of these papers is:
For some use cases, it is important to ensure Music Information Retrieval (MIR) systems are reproducing "ground truth" for the right reasons: Here's how.

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