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Poster
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
Gaël Letarte · Pascal Germain · Benjamin Guedj · Francois Laviolette

Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #218
in Theory -- Learning Theory »

We present a comprehensive study of multilayer neural networks with binary activation, relying on the PAC-Bayesian theory. Our contributions are twofold: (i) we develop an end-to-end framework to train a binary activated deep neural network, (ii) we provide nonvacuous PAC-Bayesian generalization bounds for binary activated deep neural networks. Our results are obtained by minimizing the expected loss of an architecture-dependent aggregation of binary activated deep neural networks. Our analysis inherently overcomes the fact that binary activation function is non-differentiable. The performance of our approach is assessed on a thorough numerical experiment protocol on real-life datasets.

Keywords: Theory -> Learning Theory · Deep Learning

Author Information

Gaël Letarte (Université Laval)
Pascal Germain (INRIA)
Benjamin Guedj (Inria & University College London)

Benjamin Guedj is a tenured research scientist at Inria since 2014, member of the MODAL project-team (MOdels for Data Analysis and Learning) of the Lille - Nord Europe research centre in France. He is also affiliated with the mathematics department of the University of Lille. He obtained his Ph.D. in mathematics in 2013 from UPMC (Université Pierre & Marie Curie, France) under the supervision of Gérard Biau and Éric Moulines. Prior to that, he was a research assistant at DTU Compute (Denmark). His main line of research is in statistical machine learning, both from theoretical and algorithmic perspectives. He is primarily interested in the design, analysis and implementation of statistical machine learning methods for high dimensional problems, mainly using the PAC-Bayesian theory.

Francois Laviolette (Université Laval)

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