%0 Generic %1 ravanbakhsh2015stochastic %A Ravanbakhsh, Siamak %A Poczos, Barnabas %A Schneider, Jeff %A Schuurmans, Dale %A Greiner, Russell %D 2015 %K acreuser deeplearning tutorial %T Stochastic Neural Networks with Monotonic Activation Functions %U http://arxiv.org/abs/1601.00034 %X We propose a Laplace approximation that creates a stochastic unit from any smooth monotonic activation function, using only Gaussian noise. This paper investigates the application of this stochastic approximation in training a family of Restricted Boltzmann Machines (RBM) that are closely linked to Bregman divergences. This family, that we call exponential family RBM (Exp-RBM), is a subset of the exponential family Harmoniums that expresses family members through a choice of smooth monotonic non-linearity for each neuron. Using contrastive divergence along with our Gaussian approximation, we show that Exp-RBM can learn useful representations using novel stochastic units.

@misc{ravanbakhsh2015stochastic, abstract = {We propose a Laplace approximation that creates a stochastic unit from any smooth monotonic activation function, using only Gaussian noise. This paper investigates the application of this stochastic approximation in training a family of Restricted Boltzmann Machines (RBM) that are closely linked to Bregman divergences. This family, that we call exponential family RBM (Exp-RBM), is a subset of the exponential family Harmoniums that expresses family members through a choice of smooth monotonic non-linearity for each neuron. Using contrastive divergence along with our Gaussian approximation, we show that Exp-RBM can learn useful representations using novel stochastic units.}, added-at = {2016-04-15T06:55:03.000+0200}, author = {Ravanbakhsh, Siamak and Poczos, Barnabas and Schneider, Jeff and Schuurmans, Dale and Greiner, Russell}, biburl = {https://www.bibsonomy.org/bibtex/281769fd22f517fb3f0bd34e1f217987b/pixor}, description = {1601.00034v2.pdf}, interhash = {7aa66d6b80a277112d6b6b75e742efdd}, intrahash = {81769fd22f517fb3f0bd34e1f217987b}, keywords = {acreuser deeplearning tutorial}, note = {cite arxiv:1601.00034v2.pdfComment: AISTATS 2016}, timestamp = {2016-04-15T06:55:03.000+0200}, title = {Stochastic Neural Networks with Monotonic Activation Functions}, url = {http://arxiv.org/abs/1601.00034}, year = 2015 }