Do Deep Nets Really Need To Be Deep?

Currently, deep neural networks are the state of the art on problems such as speech recognition and computer vision. We show that by using a method called model compression that shallow feed-forward nets can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models. Moreover, in some cases the shallow neural nets can learn these deep functions using the same number of parameters as the original deep models. On the TIMIT phoneme recognition and CIFAR-10 image recognition tasks, shallow nets can be trained that perform similarly to complex, well-engineered, deeper convolutional architectures. Our success in training shallow neural nets to mimic deeper models suggests that there may be better algorithms for training shallow nets than those currently available. I’ll also briefly discuss work we’re doing to compress extremely large deep models and ensembles of deep models to “modest-size” deep models that fit on servers, and to “small” deep models that run on mobile devices.

Speaker Details

Rich Caruana is a Senior Researcher at Microsoft Research. Before joining Microsoft, Rich was on the faculty at the Computer Science Department at Cornell University, at UCLA’s Medical School, and at CMU’s Center for Learning and Discovery (CALD). Rich’s Ph.D. is from Carnegie Mellon University, where he worked with Tom Mitchell and Herb Simon. His thesis on Multi-Task Learning helped generate interest in a new subfield of machine learning called Transfer Learning. Rich received an NSF CAREER Award in 2004 (for Meta Clustering), best paper awards in 2005 (with Alex Niculescu-Mizil), 2007 (with Daria Sorokina), and 2014 (with Todd Kulesza, Saleema Amershi, Danyel Fisher, and Denis Charles), co-chaired KDD in 2007 (with Xindong Wu), and serves as area chair for NIPS, ICML, and KDD. His current research focus is on learning for medical decision making, deep learning, adaptive clustering, and computational ecology.

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Rich Caruana