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29 views

Online Machine Learning of sequential events with varying delay

Lets say we have A to Z features which repeat sequentially. So you have A(1), B(1), ... Z(1) at time 1 followed by A(2), B(2),....Z(2) at time 2 and so on till A(n), B(n), ... Z(n) at time n. Each of ...
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1answer
26 views

how do you handle a “none of these” class in a CNN

It is the closed-world assumption of a CNN. For example I have trained a CNN to recognize, sedans, jeeps, trucks, suvs and crossovers, and I present an airplane it tries to fit it into of these 5. How ...
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1answer
72 views

how to handle small datasets with large dimensions

I have 48 samples which are case and control and 27000 features for each sample so my matrix is [48 X 27000]and I am using Deep belief networks(DBN) as my algorithm to predict the accuracy of the ...
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0answers
15 views

Use deep belief networks for unsupervised anomaly detection

I am working on anomaly detection on data with a large number of variables (>50) with continuous values. As I have read that deep belief networks can be used for unsupervised anomaly detection on ...
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0answers
21 views

Greedy Training of Deep Belief Networks

I try to understand the justification of Greedy Training for Deep Belief Networks. I read the tutorial at http://deeplearning.net/tutorial/DBN.html and various papers of Hinton,Bengio and other ...
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1answer
59 views

Dimensionality reduction: RBM autoencoders vs. de-noising autoencoders

I am looking at non-linear dimensionality reduction techniques and am currently trying to understand the practical differences between different autoencoder approaches: Can somebody point me to a ...
4
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2answers
81 views

The bottleneck of applying deep learning in practice

After reading a lot of deep learning papers, a kind of rough feeling is that there exist a lot of tricks in training the network to get the better-than-normal performance. From an industry application ...
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1answer
139 views

What is the best Deep Learning Library in R? [closed]

I am looking for a complete deep learning library in R. I am trying to find one or more libraries to implement: Recurrent NN Deep Belief NN Convolutional NN I have tried multiple libraries such as ...
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0answers
119 views

normalization of input to a deep belief network

I am playing around with deep belief networks, and am unsure what the best normalization scheme is. (Note that by this I mean a deep network that is trained in a greedy layer-wise manner, by ...
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0answers
54 views

What is relation between RBM and DBN? [duplicate]

What is relation between Restricted Boltzmann Machine, Deep Belief Networks, Deep Boltzmann Machines? Are they related to deep learning or to graphical models(Probabilistic Graphical Models)? ...
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0answers
20 views

How to add extra layer of MLP to DBN

I am trying to add MLP layer to DBN that can use final parameters of DBN model as Input for MLP model. I am new to python so am not well versed with its input and output processes. Any help is ...
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0answers
9 views

Rea-time or batch unsupervised techique & tool for a high-dimensional binary data

Its a theoretical question, no real data just yet to access. Hence, I cannot tell the application. Data have thousands of features an billion instances. In addition, every instance has a unique ...
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0answers
26 views

What is negative reconstruction error for Deep Belief Network?

I was studying Deep Belief Networks and started testing a small example. For the sake of these question consider the one at deeplearning tutorials Observation 1: I noticed that my reconstruction ...
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1answer
80 views

Why are Hinton's multilayer deep-learning networks stochastic?

First I'll sum up my intuitive (beginner) understanding of his deep-learning architecture. A short summary can be listened to on Coursera in the 5 minute video. We start with several layers of ...
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0answers
63 views

How is free energy an unnormalized conditional log-probability?

I am following Bengio's Learning Deep Architectures for AI and at page 28 there is a phrase that confuses me: $a(x)$ is the discriminant function or an unnormalized conditional log-probability, ...
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0answers
61 views

Why is my DBN predict only 2 out of 5 classes?

I'm using the Deeplearning.net DBN tutorial to train my data set. I normalize the feature set to zero-mean-unit-variance. However, I can only get the network to predict 2 out 5 classes even though the ...
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0answers
79 views

Difference in training procedure for DBN and DBM

This is related to the following thread Deep belief networks or Deep Boltzmann Machines? but it doesn't seem to answer in a practical sense what the difference is. So I gather a DBN is directed and ...
2
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0answers
105 views

Deep Learning with few features available

I was asked to employ deep learning on some seismic simulation data. Visually, the data is a cube, 1000 x 1000 x 1000. For each point in the cube, there are 3 numeric features [1, 0]. Some of it is ...
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1answer
229 views

Updating bias with RBMs (Restricted Boltzmann Machines)

Am very new to RBMs, trying to write an RBM program now. Sorry if this is a silly question and/or answered on here already. I've read a few articles online, and questions on here, but I can't find ...
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1answer
131 views

Need pointers to deep learning tutorials [closed]

I'm looking for good study material about deep belief networks, with particular emphasis to classification and feature extraction tasks for non-image data. I don't seem to find a great deal about ...
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2answers
3k views

What is the architecture of a Stacked Convolutional Autoencoder

So I am trying to do pre training on images of humans using convolutional nets. I read the papers http://people.idsia.ch/~ciresan/data/icann2011.pdf and ...
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0answers
234 views

Threshold on tanh or sigmoid in Convolutional neural network

I have read several papers on Convolutional Neural Nets but I am yet to come across any that has used thresholds on tanh or sigmoid to decide whether the neuron will fire or not. Obviously this works ...
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2answers
472 views

How to normalize filters in convolutional neural networks?

Usually, when convolving images, the elements in the filter sum to one. Is this criterion enforced in convolutional neural networks? If yes, how?
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0answers
87 views

A paper that proves using the latent features of RBM as input to logistic regression?

I'm looking for a paper that includes a proof that simply training a Restricted Boltzmann Machine and then using the latent features as input to a logistic regression classifier is a correct thing and ...
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0answers
114 views

Why is backpropagation used more for fine-tuning than the up-down algorithm for deep belief networks?

Deep belief networks are pre-trained using RBMs then fine tuned for a supervised learning task. For almost every paper that I have read, I have seen back-propagation used instead of the up-down ...
2
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1answer
339 views

When should we use Gibbs Sampling in a deep belief network? Before or after fine-tuning?

Gibbs sampling allows for sampling a vector with a deep belief network. There are two steps to training a DBN for a supervised learning task: greedy unsupervised pre-training and supervised ...
2
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1answer
53 views

Using Gibbs Sampling on Deep Belief Network with PCA [closed]

I'll make this question as clear as possible: If I were to PCA my data onto say 300 Principal components. Then train a deep belief network with 300 input features. Would I still be able to sample ...
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0answers
127 views

Using deep learning for time series prediction with uncertain time series window size!

I'm new in area of deep learning and I am trying to use deep learning to do prediction on machine generated log data gathered as stream of data. I have seen LSTM an how it can be helpful to train ...
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1answer
345 views

Deep learning algorithm

What's the difference between deep belief network and deep convex network?
2
votes
1answer
428 views

number of feature maps in convolutional neural networks

When learning convolutional neural network, I have questions regarding the following figure. 1) C1 in the layer 1 has 6 feature maps, does that mean there are six convolutional kernals? Each ...
2
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1answer
256 views

Do deep belief networks minimize required domain expertise, pre-preprocessing, and selection of features?

I'm trying to get a basic layman's grasp of deep belief networks and deep learning in general. I've read a few papers and watched a few presentations, but there's one aspect I'm hoping someone can ...
2
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1answer
146 views

Scale invariance for images

Given that images can be of vastly different resolutions, but neural networks are usually presented as having a fixed number of inputs, what are the standard techniques used to handle the difference ...
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0answers
360 views

Where can I find a MATLAB implementation of Convolutional Deep Belief Network?

I have been trying to find a MATLAB implementation of the Convolutional Deep Belief Network. A Google search returned libraries that implement a Convolutional Restricted Boltzmann machine. I am aware ...
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0answers
175 views

What are some of the image classification datasets other than MNIST on which Deep Belief Network (DBN) has produced good results?

What are some of the image classification datasets other than MNIST on which Deep Belief Network (DBN) has produced state-of-the-art results? Even if its not state-of-the-art, but, I am looking for ...
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0answers
108 views

What is “Hierarchical Probabilistic Inference” in Honglak Lee's C-DBN?

This question is based on Honglak Lee's paper "Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations". I have implemented a convolutional RBM with ...
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4answers
21k views

What is the difference between convolutional neural networks, restricted Boltzmann machines, and auto-encoders?

Recently I have been reading about deep learning and I am confused about the terms (or say technologies). What is the difference between convolutional neural networks (CNN), restricted Boltzmann ...
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2answers
206 views

How to derive the gradient formula for the Maximum Likelihood in RBM?

I am learning RBM (restricted Boltzmann machine) for deep learning. The log-likelihood of RBM is given as : $$\ln(L(\theta|v))=\ln(p(v|\theta))=\ln\frac{1}{Z}\sum_h e^{-E(v,h)}=\ln\sum_h ...
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2answers
2k views

In convolutional neural networks, how to prevent the overfitting?

Given certain amount of labeled data, we define the net structure, such as number of layers, types of layers, the number of convolutional layers, the number of pooling layers, etc. And train the ...
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2answers
1k views

Sparse Autoencoder [Hyper]parameters

I have just started using the autoencoder package in R. http://cran.r-project.org/web/packages/autoencoder/index.html Inputs to the autoencode() function include lambda, beta, rho and epsilon. What ...
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0answers
67 views

Is deep belief network a belief network [closed]

I am new to deep learning. Please help me.. What is belief network. What is the use of it? How to learn belief networks? Is deep belief network a belief network with multiple layers?
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1answer
367 views

Summarization of text documents (legal domain) using deep learning techniques

I am referring to the site deeplearning.net on how to implement the deep learning architectures. I have read quite a few research papers on document summarization (both single document and ...
0
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1answer
229 views

definition of deep belief network

I was studying Deep Belief Network (DBN) and have questions. 1) According to the definition of DBN, DBN is formed by stacking RBM on top of each other such that the hidden layer in a lower layer ...
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3answers
2k views

Guideline to select the hyperparameters in Deep Learning

I'm looking for a paper that could help in giving a guideline on how to choose the hyperparameters of a deep architecture, like stacked auto-encoders or deep believe networks. There are a lot of ...
0
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1answer
174 views

Comparing different deep learning models?

Does anyone know a paper that describes the differences and compares the different deep learning architectures? like Stacked autoencoders, deep believe networks, maxout networks ... etc.
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2answers
2k views

Deep belief networks or Deep Boltzmann Machines?

I'm kinda confused. Is there a difference between Deep belief networks and Deep Boltzmann Machines? Are they different stuff or same thing?! If so, what's the difference?
4
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2answers
565 views

Is a Gaussian-Gaussian RBM just a linear model?

The 'conventional' configuration of RBMs are Binary-Binary and Gaussian-Binary (and sometimes Binary-Gaussian) units. Although it is possible for both the visible and hidden units to be gaussian, ...
3
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1answer
722 views

Are Restricted Boltzmann Machines better than Stacked Auto encoders and why?

So I'm learning about deep learning. I first learned about stacked auto-encoders and now I'm learning about Restricted Boltzmann Machines. However non in the papers/tutorials I read I found them ...
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0answers
81 views

Rough estimates for training time of deep belief networks

I'm still learning about deep learning. However I'm currently interested to know if deep learning architectures scale well or not. Suppose I have a dataset with 1 million training examples, can you ...
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1answer
320 views

Why features compression is good?

I'm reading about deep learning and that in principles it's a features compression technique and that is why it works. Now my question is why compressing features from 200 or so into 4 is better? How ...
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0answers
76 views

Deep Belief Networks: connecting visible bias of higher layers to hidden bias of lower layer?

Suppose we are building a DBN (Deep Belief Network) and we have already trained some lower layers as Restricted Bolzmann Machines. Now we add a new layer, with new weights and new biases for the new ...