Questions tagged [deep-network]

For questions about deep neural networks (DNNs), neural networks with multiple hidden layers between the input and output layer.

31 questions with no upvoted or accepted answers
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4
votes
2answers
37 views

What kind of output should be used for predicting angles in DNNs?

I am building a model which predicts angles as output. What are the different kinds of outputs that can be used to predict angles? For example, output the angle in radians cyclic nature of the ...
4
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0answers
476 views

Sparsity constraint in a deep autoencoder

Is there any way and any reason why one would introduce a sparsity constraint on a deep autoencoder? In particular, in deep autoencoders the first layer often has more units than the dimensionality ...
3
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0answers
38 views

Are there any commonly used discontinuous activation functions?

Are there any commonly used activation functions (e.g. that take values in $(0,.5)\cup (.5,1)$)? Preferably for classification? Why? I was looking for commonly used activation functions on Google, ...
3
votes
1answer
57 views

Is batch normalization not suitable for non-gaussian input?

I generate some non-Gaussian data, and use two kinds of DNN models, one with BN and the other without BN. I find that the model DNN with BN can't predict well. The codes is shown as follow: <...
3
votes
1answer
129 views

Is it possible to create a decompiler using AI?

I am trying to decode a compiled file to source code and I am failing. I want to know whether an AI based decompilation is possible for a compiled files? Is it possible to create a decompiler using a ...
2
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0answers
23 views

Why Pixel RNN (Row LSTM) can capture triangular contexts?

I'm reading the paper Pixel Recurrent Neural Network. I have a question about Row LSTM. Why Row LSTM can capture triangular contexts? In this paper, the kernel of the one-dimensional convolution ...
2
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0answers
42 views

Which deep neural networks are appropriate for the detection of bombs?

This is a follow-up question from my previous post here about explosion detection. I gathered a dataset of explosions. As I'm new to Deep Learning in Keras, I'm trying to see what architecture best ...
2
votes
1answer
55 views

Spikes in of Train and Test error

I learn a DNN for image recognition. During each epoch, I calculate mean loss in the training set. After each epoch, I calculate loss and number of errors over both training and test set. The problem ...
2
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0answers
58 views

How to define cost function for custom nonlinear functions?

For logistic regression, the Cost function is defined as: \begin{equation} Cost(h_{\theta}(x)-y) = -ylog(h_{\theta}(x))-(1-y)log(1-h_{\theta}(x)) \end{equation} I now have a nonlinear function \begin{...
2
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0answers
24 views

Using two generative adversarial nets to classify articles - what is a good approach?

I'm trying to create a deep learning network to classify news article based on the text and associated image. The idea comes from a novel use of GANs to classify based on generated data. My approach ...
2
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0answers
150 views

Deep learning model (LSTM) with temporal and non temporal attributes

I'm working on a project to predict the usage of all the files in a filesystem in near future based on the metadata of the file system for past 6 months. I've got the following attributes about the ...
2
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0answers
75 views

How would I implement this New Type of NN

CIO NN CIO NN stands for Controller Input Output Nerual Network note due to a typo the "nearon" means "neron" For this we have to redefine the Nearon 2 Inputs 2 Outputs 4 Weights (each input and ...
1
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0answers
18 views

How can I find the similar non-zero connections between different levels of sparsity of the same network?

I am pruning a neural network (CNN and Dense) and for different sparsity levels, I have different sub-networks. Say for sparsity levels of 20%, 40%, 60% and 80%, I have 4 different sub-networks. Now, ...
1
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0answers
21 views

Steps to train and re-train a good model

I'm still a bit new to deep learning. What I'm still struggling, is what is the best practice in re-training a good model over time? I've trained a deep model for my binary classification problem (...
1
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0answers
16 views

Post-classification after inference

I designed a fire detection using Deep Learning based classification approach. In my training dataset, I have both fire and fire smokes are supposed to be detected (all under "fire"; mostly real fires ...
1
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0answers
31 views

Relationship between model complexity (depth) and dataset size

I'm new to deep learning. I was wondering what's the relationship between a deep model complexity (e.g. total number of parameters, or depth) and the dataset size? Assuming I want to do a binary ...
1
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0answers
13 views

Camera pose to environment Mapping

I would like to teach a model the environment of a room. I'm doing so by mapping a camera pose (x, y, z, q0, q1, q2, q3) to its corresponding image; where x, y, z represent location in Cartesian ...
1
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0answers
31 views

One end to end Neural network or many task-specific ones?

Is it better to train one neural network for a dispersed labeled data with large number of classes or first classify data by unsupervised learning then train each part by a separate NN? I mean by ...
1
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0answers
40 views

What is the feasible neural network structure that can learn to identify types of trajectory of moving dots?

I have multiple image sequences, each of which contains an animation of two moving dots. The trajectory of the dots in a sequence is always cyclic (not necessarily circular). There are two types of ...
1
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0answers
41 views

How to handle varying length of inputs that represent dependencies and recursivity in deep neural networks in case of regression?

I wanna solve a problem of regression to predict a factor. I decide to go with Deep Neural Networks as solution for my problem. The features in this problem represent loop characteristic such us loop ...
1
vote
3answers
509 views

How to create Partially Connected NNs with prespecified connections using Tensorflow?

I'd like to implement a partially connected neural network with ~3 to 4 hidden layers (a sparse deep neural network?) where I can specify which node connects to which node from the previous/next layer....
1
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0answers
78 views

Confidence interval around a DNN prediction

I am facing a problem and do not know whether it is even solvable: I want to predict the behaviour of a system using a DNN, say a CNN, in the sense that I want to predict the time and intensity of a ...
1
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0answers
26 views

Are there a finite set of computable functions constructing deep neural network which can form or implement any c.e. function or computable function?

Are there a finite set of computable functions constructing deep neural network which can form or implement any c.e. function or computable function? Or does there exist a finite set of computable ...
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0answers
32 views

Understanding the role of the target network in this DQN algorithm

I've found online this interesting algorithm: From what I understand reading this algorithm, I can't figure out why I should "perform the opposite action" and consequently storing that second ...
0
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0answers
8 views

How to stabilize the training of a Conv-Siamese Neural Network if the results after different trainings vary relatively strongly?

I am training a neural network using MSE and ADAM optimizer. More precisely, a siamese architecture with a convolutional encoder and euclidean distance on top. I am using MSE because I have different ...
0
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0answers
11 views

Deep Network with constraint or auxiliary features

The target of my current neural network is to predict a label. The dataset contains some features, there is a label $y_i$ in transaction $i$, indicating its classification. There is one feature $f^{i}...
0
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0answers
9 views

Can a trained Vid2Vid model be run on AMDs Ryzen 2700x with 32GB of RAM?

I know that training deep neural networks (DNNs) takes a lot of computational resources. This is, of course, just a generalized statement. Different networks require different resources. One that I ...
0
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0answers
100 views

Should I use deep learning to solve my task?

I need to predict the performance (CPI cycles-per-instruction) of 90 machines for the next hour (or day). Each machine has a thousand records (e.g. CPU and memory usage). Currently, I am using a ...
0
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1answer
71 views

Does higher Accuracy in Reinforcement Learning indicate better model performance?

If a reinforcement learning algorithm uses a Deep Neural Network to predict the action given a state (a NN for a policy function), an Monte Carlo Tree Search in a model-based learning setup, then ...
0
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0answers
26 views

Stacking layers with different input size in deep network

I am trying to design a deep network that works on signals. The network should include multiple stacked tasks, but each task would work on a different window size of the signal. For example, the ...
0
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3answers
95 views

Is there a way of pre-determining whether a CNN model will perform better than another?

I developed a CNN for image analysis. I've around 100K labeled images. I'm getting a accuracy around 85% and a validation accuracy around 82%, so it looks like the model generalize better than ...