Jul 8, 2016 - allows more effective generalization capability, less bias towards data ... Boltzmann machine; deep neural networks; low-resource tasks. 1. .... learning can be further incorporated into deep architectures by ..... grid search from 0.0
higher-layer units to cover larger areas of the input in a ... of binary hidden units h, a set of (binary or real- ..... 3http://www.cnbc.cmu.edu/cplab/data_kyoto.html.
1 for an illustration) and y1 is a random variable indi- cating the presence of a lip in the window defined by θ given a specific lip stage. The lip stages represent a ...
Mar 23, 2018 - task of clustering; (4) by applying layer-wise training and supervised fine-tuning, a deep ... Keywords: underwater acoustics; machine learning; deep learning; hydrophone. 1. Introduction ... physical condition of human beings.
best such measure has proven to be spam filtering. There are two general approaches to mail filtering: knowledge engineering (KE) and machine learning (ML).
May 25, 2018 - MNIST digit recognition consists 784 nodes in visible layer to han- dle 28 Ã 28 pixels of the input images, and 10 nodes in hidden layer.
Faezeh Movahedi and Ervin Sejdic are with the Department of Electrical ... orders, School of Health and Rehabilitation Sciences, University of Pittsburgh,.
Deep Belief Networks (DBNs) are multi-layer generative models. They can be ... speaker adaptive and discriminative features as inputs to the DBN. 2. FEATURE ...
Two different architectures for the proposed DBN-WP have empirically selected composed of. RBMs. In order to test their performance, both 5-fold and hold-out ...
Mar 26, 2014 - Florian Raudies1,2*, Eric A. Zilli3, Michael E. Hasselmo1,2,4. 1 Center for ..... For the contrastive divergence (CD) algorithm we run a Gibbs.
ing sub-areas of Machine Learning: deep networks [Lee et al., 2009b], [Lee et al., ... proach is inspired by and extends the idea of penalty logic. [Pinkas, 1995] ...
Jul 9, 2015 - spike-based hardware platforms offers an alternative for running deep neural ...... Das, S., Pedroni, B. U., Merolla, P., Arthur, J., Cassidy, A. S., ...
Feb 22, 2013 - ... that sparsity is an integral process in the hierarchical processing of visual information [??? ]. 1. arXiv:1301.3533v2 [cs.NE] 22 Feb 2013 ...
Jul 9, 2015 - John V. Arthur,. IBM Almaden .... and communication (O'Connor et al., 2013). Furthermore .... is described in detail in O'Connor et al. (2013).
The Deep Belief Network (DBN) (Hinton et al., 2006) and Deep Boltzmann ..... spective of learning, we desire distributions on the model parameters {W(l)} and {c(l)}, ..... mented in a novel way by introducing auxiliary Pólya-. Gamma variables.
Dec 3, 2009 - We apply deep belief networks of restricted Boltzmann machines to bags of ... More generally the image is broken up into 2x2 or 4x4 regions, ...
If the batch size is too small, parallelization of matrix- ... replaced by another randomly initialized hidden layer and
cer research fund, 2014). Breast cancer represents 18.3% of the total cancer cases in Egypt. A percentage of 37.3% of breast cancer could be fully healed ...
Keywords: deep belief networks, deep learning, drug discovery, virtual screening. ... Therefore, a computer-aided drug design is done to help reducing the time ...
3Department of Computer Science, University of Toronto, Canada. 1{tsainath, bedk .... GMM/HMM systems for a variety of f
Deep Belief Networks Intro to Deep Neural Networks 26th to 27th August 2016 Supervised By Dr. Asifullah Presented By Muhammad Islam (DCIS, PIEAS)
Pattern Recognition Lab Department of Computer Science & Information Sciences Pakistan Institute of Engineering & Applied Sciences
Motivation: Applications of DBN’s Object Recognition
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Applications of DBN’s (cont…) • Image Retrieval
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Applications of DBN’s (cont…) • Document Modeling
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Applications of DBN’s (cont…) • Document Retrieval
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Background • Deep neural networks were not absent before 2000
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Background • However, training deep networks was quite difficult
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Background • Hence other simple algorithms prevailed
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Background • Now the situation has changed
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Background • Deep belief Networks became popular in 2006 • Most prominent work done by Geoffrey Hinton • There were a lot of research
• And now more powerful tools exist
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Introduction • Deep belief Networks are basically Directed Graphs 2000 units
• Built in the form of stacks using individual units called Restricted Boltzmann Machines
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500 units 500 units
28 x 28 pixel image 11
Introduction • Keyword “Belief” indicates an important property
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Boltzmann Machines • Stochastic generative model • estimate the distribution of observations(say p(image)), instead of their classification p(label|image) • One input layer and one hidden layer • Defined Energy of the network and Probability of a unit’s state Deep Belief Network
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Restricted Boltzmann Machines • Feed-forward graph structure with two layers • visible layer (binary or Gaussian units) and hidden layer (usually binary units) • No intra layer connections • Visible units and hidden units are conditionally independent Deep Belief Network
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BM vs RBM
Hidden layer, h
Visible layer, v
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Restricted Boltzmann Machines • Two characters define an RBM:
• states of all the units: obtained through probability distribution.
• weights of the network: obtained through training (Contrastive Divergence) Deep Belief Network
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Restricted Boltzmann Machines • Energy is defined for the RBM as:
E (v, h) ai vi b j h j h j wi , j vi i
j
i
j
Where E is the energy for given RBM and ai , bi and Wi represent weights for hidden layer bias, weights for visible layer bias and combined weights respectively.
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Restricted Boltzmann Machines • Distribution of visible layer of the RBM is given by 1 P (v ) e E ( v , h ) Z h
Where Z is the partition function defined as the sum of E ( v ,h ) over all possible configurations of {v,h} e • Probability that a hidden unit i is on(binary state 1) is m
P(h j 1 | v) (b j wi , j vi ) i 1
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Restricted Boltzmann Machines for calculating a particular weight between two units
logp (v) vi h j data vi h j model wij and
logp (v) wij w ij
hence
wij ( vi h j data vi h j model ) Deep Belief Network
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Training an RBM
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Contrastive Divergence
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Training DBN’s • First train a layer of features that receive input directly from the pixels. • Then treat the activations of the trained features as if they were pixels and learn features of features in a second hidden layer.
• It can be proved that each time we add another layer of features we improve a variational lower bound on the log probability of the training data. Deep Belief Network
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Training DBN’s
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References •
DBN lecture by Geoffrey Hinton; Vedios and slides at http://videolectures.net/mlss09uk_hinton_dbn/
