Why explaining away concept is not applicable in restricted Boltzmann machines? Their hidden units form a V structure from which probabilistic influence can flow given the observed visible variable. Why is this a problem in deep belief nets?
I am studying deep learning and the deepnet R package gives me the following example: (rbm.up function Infer hidden units states by visible units) library(deepnet) Var1 <- c(rep(1, 50), rep(0, 50)) Var2 <- c(rep(0, 50), rep(1, 50)) x3 <- matrix(c(Var1, Var2), nrow = 100, ncol = 2) r1 <- rbm.train(x3, 3, numepochs = 20, cd = 10) v <- c(0.2, 0.8) h <- rbm.up(r1, v) h The result: [,1] [,2] [,3] [1,] 0.5617376 0.4385311 0.5875892 What do these results means?
I want to use DBN to reduce the 41 features of nslkdd dataset after transforming nominal data to numeric the number of features increases from 41 to 121 . I used 3 RBMs (121-50-10) now I want to know the 10 selected features i.e know their names to put them as an input to the classifier. how can I do it?
I am having a hard time understanding the strategy for inputting the color. Most tutorials on RBMs only train grayscale images. If the image is grayscale, the input units can be binary, and I can normalize the gray scale value to [0,1], and then treat them like probabilities in the input layer. Or whiten the dataset and use Gaussian units in the input layer. How do I treat color images? Obviously, the input units cannot be binary - unless I …
I'm working with a large dataset (about 50K observations x 11K features) and I'd like to reduce the dimensionality. This will eventually be used for multi-class classification, so I'd like to extract features that are useful for separating the data. Thus far, I've tried PCA (performed OK with an overall accuracy in Linear SVM of about 70%), LDA (performed with very high training accuracy of about 96% but testing accuracy was about 61%), and an autoencoder (3 layer dense encoder …
I have text data representing sensor outputs. Dataset: 1458996986002; 11.43,-15.86,11.20,508.26; -1.59,-0.22,6.17,40.68; 126.0,-150.9,-105.0,49671.81; Walk 1459002923002; 16.69,-12.68,13.96,634.65; -2.55,2.13,4.87,34.87; 126.0,-150.9,-105.0,49671.81; Walk timestamp; acc_x,acc_y,acc_z; gyro_x,gyro_y,gyro_z; magn_x,magn_y,magn_z; ActivityName My Goal: I would like to extract features from the text lines before feeding it into a Recurrent Neural Network (GRU/LSTM). So, my goal is automatic feature extraction. Those extracted features (encoder network) will be used before the neural network for an activity recognition task (classification). My Question: Which Autoencoder (denoising, variational, sparse) is suitable for such …
Many tutorials suggest that after training a RBM, one can have a good reconstruction of training data just like an autoencoder. An example tutorial. But the training process of RBM is essentially to maximize the likelihood of the training data. We usually use some technique like CD-K or PCD, so it seems that we can only say that a trained RBM has high probability to generate data which is like training data (digits if we use MNIST), but not correspond …
Why are the parameters of a Restricted Boltzmann machine trained for a fixed number of iterations (epochs) in many papers instead of choosing the ones corresponding to a stationary point of the likelihood? Denote the observable data by $x$, hidden data by $h$, the energy function by $E$ and the normalizing constant by $Z$. The probability of $x$ is: \begin{equation} P(x) = \sum_h P(x,h) = \sum_h \frac{e^{-E(x,h)}}{Z}. \end{equation} The goal is to maximize the probability of $x$ conditional on the …
Restricted Boltzmann machines are stochastic neural networks. The neurons form a complete bipartite graph of visible units and hidden units. The "restricted" is exactly the bipartite property: There may not be a connection between any two visible units and there may not be a connection between two hidden units. Restricted Boltzmann machines are trained with Contrastive Divergence (CD-k, see A Practical Guide to Training Restricted Boltzmann Machines). Now I wonder: How are non-restricted Boltzmann Machines trained? When I google for …
I am studying Restricted Boltzmann Machines (RBMs), and it is described as a symmetrical bipartite graph. Link How is this different from a Complete bipartite graph? They seem to be the same to me, which is why I'm curious to why there is such a clear difference in terminology.
These are 4 different weight matrices that I got after training a restricted Boltzman machine (RBM) with ~4k visible units and only 96 hidden units/weight vectors. As you can see, weights are extremely similar - even black pixels on the face are reproduced. The other 92 vectors are very similar too, though none of weights are exactly the same. I can overcome this by increasing number of weight vectors to 512 or more. But I encountered this problem several times …
I’m trying to understand, and eventually build a Restricted Boltzmann Machine. I understand that the update rule - that is the algorithm used to change the weights - is something called “contrastive divergence”. I looked this up on Wikipedia and found these steps: Take a training sample v, compute the probabilities of the hidden units and sample a hidden activation vector h from this probability distribution. Compute the outer product of v and h and call this the positive gradient. …
I was following a tutorial on understanding Restricted Boltzmann Machines (RBMs) and I noticed that they used both the terms reconstruction and backpropagation to describe the process of updating weights. They seemed to use reconstruction when referring to the links between the input and the first hidden layer and then backpropagation when referring to the links to the output layer. Are these terms used interchangeably or are they different concepts?
At the moment I'm playing with Restricted Boltzmann Machines and since I'm at it I would like try to classify handwritten digits with it. The model I created is now a quite fancy generative model but I don't know how to go further with it. In this article the author say, that after creating a good generative model, one "then trains a discriminative classifier (i.e., linear classifier, Support Vector Machine) on top of the RBM using the labelled samples" and …
Assume that we have a large corpus of texts to train with. Given N words as input, I want to model the joint probability $p(x_1, x_2, ..., x_N)$ of these words appearing together in a sentence. More specifically, the N words are not required to be ordered or contiguous, and words other than given words can appear in the sentence. There is no restriction on the number of times each of N words can appear in the sentence. I did …
I'm not sure how to implement this architecture. I'm following this thesis (pages 17-19) or this paper but I'm not sure how to train it. I want to use this to extract features from raw audio. I know I have to compute the positive and negative correlations, but I don't know how to do this exactly since I can not find any detailed documentation of this. What I have done so far is: Positive correlation To compute it I do …
I am learning about the Boltzmann machine. So far, I have successfully written a code that can learn the coefficients of the energy function of a Restricted Boltzmann Machine. Now, since my model is generative (if I have understood things correctly so far) and I know for sure that RBMs can be used for inpainting in binary images at least, I want to know how I can generate a sample from my probabilistic distribution given by the Boltzmann machine. That …
Using RBMs to pre-train a deep net as in this example RBM, the activation function is sigmoid and makes the math much easier. What are the implications after the initial weights are learned using sigmoid activation functions to switch to ReLU for the train phase? I suppose that using tanh in either phase (pre-train or train) and sigmoid or ReLU in the other would cause great problems, but since ReLU and sigmoid are similar for small values, would it still …
In neural networks and old classification methods, we usually construct an objective function to achieve dimensionality reduction. But Deep Belief Networks (DBN) with Restricted Boltzmann Machines (RBM) learn the data structure through unsupervised learning. How does it achieve dimensionality reduction without knowing the ground truth and constructing an objective function?