Questions tagged [deep-network]

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

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7 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 ...
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59 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 ...
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2answers
92 views

what will i be able to do in the end of AI: modern approach? [closed]

i just started the book and i was wondering , what will i be able to do in AI by the end of the book ? and more particularly, what is my position with Reinforcement Learning, deep neural networks and ...
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30 views

How many cameras does the Intel Intel Neural Compute Stick 2 support? [closed]

I am writing here to inquire about the capabilities of the Intel Neural Compute Stick 2. There are many tutorials and documentations, including articles on the compute stick 2, but I still have not ...
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1answer
40 views

What is the difference between Kaldi and DeepSpeech speech recognition systems in their approach?

I would like to know how do Kaldi and DeepSpeech speech recognition systems differ algorithmically? Which one would be more accurate for continuous speech in time?
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1answer
28 views

Techniques and semantics in better training of deep learning models

I'm relatively new to Deep Learning, and trying various models and datasets using Keras. I'm starting to love it! Through-out my experimentations, I have come into ...
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1answer
60 views

Can Google's patented ML algorithms be used commercially?

I just find that Google patents some of the widely used machine learning algorithms. For example: System and method for addressing overfitting in a neural network (Dropout?) Processing images using ...
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1answer
54 views

Is there anything theoretically revolutionary about Deep Neural Network?

In recent years we have seen quite a lot of impressive display of Deep Neural Network (DNN), as demonstrated most famously by AlphaGo and its cousin programs. But if I understand correctly, deep ...
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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 ...
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1answer
56 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 ...
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1answer
34 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: <...
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1answer
22 views

Are there deep networks that can differentiate object class from individual object?

We usually categorize objects in a hierarchy of classes. Let us say crow vs bird. In addition, classes can be "messy", for instance a crow can be also a predator, but not all birds are predators. My ...
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1answer
31 views

How to map X to Y for TensorFlow RNN training data

Usually for DNN, I have the training data of matching X (2D) to Y (2D), for example, XOR data: X = [[0,0],[0,1],[1,0],[1,1]]; Y = [[0], [1], [1], [0] ]; ...
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1answer
25 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 ...
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2answers
23 views

Is it still called linear separation with a layer of more than 1 neuron

A single neuron will be able to do linear separation. For example, XOR simulator network: ...
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1answer
42 views

How many hidden layers are needed for this training data set

I'm trying to separate classes in 3D space, the data are as in the sketch below: There are 3 classes: 0,1,2; and with the look into the sketch, it seems that I need 3 planes to separate the classes, ...
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1answer
40 views

In how few updates can a multi layer neural net be trained?

A single iteration of gradient descent can be parallelised across many worker nodes. We simple split the training set across the worker nodes, pass the parameters to each worker, each worker computes ...
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24 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 ...
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4answers
199 views

What could an oscillating training loss curve represent?

I tried to create a simple model that receives an $80 \times 130$ pixel image. I only had 35 images and 10 test images. I trained this model for a binary classification task. The architecture of the ...
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2answers
322 views

Iteratively and adaptively increasing the network size during training

For an experiment that I'm working on, I want to train a deep network in a special way. I want to initialize and train a small network first, then, in a specific way, I want to increase network depth ...
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0answers
12 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 ...
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2answers
31 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 ...
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1answer
47 views

How can I use one neural network for both players in Alpha Zero (Connect 4)?

First of all, it is great to have found this community! I am currently implementing my own Alpha Zero clone on Connect4. However, I have a mental barrier I cannot overcome. How can I use one neural ...
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1answer
29 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 ...
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1answer
26 views

Further Normalization of Standardized data - ANN

I want to develop a regression model using the artificial neural network. For developing such a model I use standardised ( z-score normalised ) data. given below is the sample data set. Here MAX is ...
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0answers
57 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{...
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0answers
27 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 ...
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2answers
76 views

How long it takes to train face recognition deep neural network? (rough estimation)

If I use a desktop PC with a GPU, how long it might take to train face recognition deep neural network on let's say dataset of 2.6 million images and 2600 identities? I guess it should depend on ...
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1answer
139 views

Can you learn parameters in nonlinear function?

In the paper Nonlinear Interference Mitigation via Deep Neural Networks, the the following network is illustrated. The network structure is The network parameters are $\theta = \{W_1^{1},...,W_1^{l-...
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38 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 ...
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0answers
40 views

How to use 0 padding with mask layer to handle variable lenght of my inputs in case of Multi-Layers Perceptrons?

We wanna build a DNN model to predict unrolling factor though our features represent variable length of inputs. Knowing that we have to give our features at once "0 padding" look like the only ...
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0answers
35 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 ...
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1answer
709 views

Suitable reward function for trading buy and sell orders

I am working to build an deep reinforcement learning agent which can place orders (i.e. limit buy and limit sell orders). The actions are ...
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1answer
105 views

Alpha zero before move 8

The Alpha zero paper says that the The first set of features are repeated for each position in a T = 8-step history. So what happens before the first 8 moves? Do they just repeat the starting position?...
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1answer
346 views

Deep Q-Learning poor convergence on Stochastic Environment

I'm trying to implement a Deep Q-network in Keras/TF that learns to play Minesweeper (our stochastic environment). I have noticed that the agent learns to play the game pretty well with both small and ...
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1answer
142 views

Significance of depth of a deep neural network

How is a feed-forward neural network with few hidden layers and lots of nodes in those hidden layers different from a network with a lot of hidden layers but relatively lesser nodes in those hidden ...
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1answer
181 views

Chess policy network

I am interested in making a simple chess engine using neural networks. I already have a fairly good value network but I can't figure out how to train a policy network. I know that Leela chess zero ...
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3answers
355 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....
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3answers
92 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 ...
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1answer
244 views

Target values of 0.1 for 0 and 0.9 for 1 for sigmoid

I recently read an article about neural networks saying that, when using sigmoid as activation function, it's advised to use 0.1 as target value instead of 0, and 0.9 instead of 1. This was to avoid "...
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0answers
22 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 ...
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0answers
75 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 ...
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1answer
2k views

Batch Normalization in Deep Autoencoders?

Does it make sense to use Batch Normalization in Deep (stacked) or/and Sparse Autoencoders? I cannot find any resources for that, so is it safe to assume that since it works for other DNNs it will ...
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0answers
401 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 ...
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0answers
134 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 ...
3
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2answers
169 views

How to build my own dataset and model for an LSTM neural network

I have a sort of mathematical problem and I'm not sure which model I should choose to make an LSTM neural network. Currently in my country, there is a system in which certain groups of researchers ...
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2answers
213 views

What do neural connection weights represent 'conceptually'?

I understand how Neural Networks work and have studied its theory well. My question is at the intricacies of Deep Neural networks and perhaps is a bit beyond common understanding (as I have been told (...
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1answer
164 views

Elon musk's comment on “non-benign AI scenarios”

I watched a youtube clip of Elon Musk talking about his view on the future of AI. He gave two examples. One of the examples was a benign scenario and the other example was a non benign scenario where ...
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1answer
138 views

Regression with more than one output, neural network

Currently in my country, there is a system in which certain groups of researchers upload information on products of scientific interest, such as research articles, books, patents, software, among ...
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1answer
445 views

what's the definition of singularity in the context of neural networks?

The following paper explains the use of skip connections to break the singularity in deep networks. But, I have not fully understood what singularity is. https://arxiv.org/pdf/1701.09175v8.pdf Any ...