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Questions tagged [neural-network]

Artificial neural networks (ANN), are composed of 'neurons' - programming constructs that mimic the properties of biological neurons. A set of weighted connections between the neurons allows information to propagate through the network to solve artificial intelligence problems without the network designer having had a model of a real system.

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Weighted MMD for InfoVAE?

I'm trying to figure out how can weighted MMD from Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation (chapter 3. Weighted Maximum Mean Discrepancy) be adapted for InfoVAE: A ...
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Training accuracy decreases

I have a program in which I use sequence to sequence approach as a prediction model with attention. The problem is, while training, the accuracy is always decreasing at each epoch, like shown in the ...
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CNN for binary classification problem

I am trying to make a convolutional neural network that classify images in two categories: with cats and without cats. It's the first time I am doing something like this and it seems I am having a ...
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Random forest vs. XGBoost vs. MLP Regressor for estimating claims costs

Context I'm building a (toy) machine learning model estimate the cost of an insurance claim (injury related). Aim is to teach myself machine learning by doing. I have settled on three algorithms to ...
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CVNN and Tensorflow

How the algorithm use Complex Value Neural Network ( all complex value, like: input, weight,bias and output) in python
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Where can I find out about the “Helvetica scenario”?

From the paper introducing GANs: It makes sense that collapsing too many $\vec{z}$-values to a single $\vec{x}$-value will cause problems. However, I was a bit confused as to how training $G$ for a ...
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Using an RNN to predict fantasy football results

So I have a couple questions about the design of a neural network. I'm trying to create a neural network to predict the number of fantasy points a player will score in a given week. First of all, I ...
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Does mean/likelihood-encoding work for neural networks?

Or is it something that only works with tree-based models?
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Philosophical question on redundancy

Suppose I implement a supervised learning version of LSTM similar to this. Namely, I have these univariate time series data: ...
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What is the best architecture for Auto-Encoder for image reconstruction?

I am trying to use Convultional Auto-Encoder for its latent space (embedding layer), specifically, I want to use the embedding for K-nearest neighbor search in the latent space (similar idea to ...
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What is used for Machine Translation besides RNN

I am doing a university report and it seems that encode-decode RNN are optimal for Machine Translation. I need something else to compare it to but I can't seem to make a proper google search for it. ...
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Are CNNs applicable on structured data?

I can use CNN to classify MNIST images, but I don't know whether CNNs are applicable on iris data as well? If not, why?
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Gradient Descent in ReLU Neural Network

I’m new to machine learning and recently facing a problem on back propagation of training a neural network using ReLU activation function shown in the figure. My problem is to update the weights ...
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Why my CNN model is not learning?

I want to train a model to predict one's emotion from the physical signals. I have a physical signal and using it as input feature; ecg(Electrocardiography) In my dataset, there are 312 total ...
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Variational auto-encoders for text generation

How are the variational auto-encoders used for text generation? Can variational auto-encoders be used for character based text generation?
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Train Keras LSTM for sequential data, where the target values for every element are given, except for the last one?

I am currently working on a data set with sequences of trips from certain people. These trips take place from one cluster to another. The starting-cluster of a trip does not always equal to the ...
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Plotting Loss on Validation Set for Text Summarization Neural Net

I am using a seq2seq encoder-decoder neural net with attention to summarize text, but I think it may be overfitting. https://github.com/dongjun-Lee/text-summarization-tensorflow Is there a way to ...
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Accuracy improving but, val_acc oscillating in ConvNet. What does it mean?

In my ConvNet model, i'm trying to classify some images. It is malware images and it doesn't contain complex features (i think), as expected model learn to classify images easily. You can see my ...
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Cant learn simple neuron network

my task is to learn simple neural network, input size 1, hidden layer size 8 and output size 1, a function f = {0->1,1->0,2->0,3->1}, but can learn the network to get satisfing result. I am using ...
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How to mathematically define the architecture of neural network model? And the function space associated with it?

My goal is to properly define a search space for neural architecture search (NAS). I think a proper definition must handle the following issues. how to mathematically quantify the topology? how to ...
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Sequence classification using oneClass SVM

In the code below, I'm using a sequence to sequence approach as a prediction model for anomaly detection, The data set I'm working with is ADFA-LD. The training phase is done using only normal ...
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How do I improve long-term memory and backwards context sensitivity of LSTM model?

I have some data, which has a lot of theory behind, explaining how it should look. Therefore it's extremely hard to sort this data by conventional methods as it ends up being a long list of if-then ...
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1answer
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How to count categorization instances in a NN?

Say one-hot encoding is the perfect way to represent a series of objects such as clothing items. Ie: A hat is [1 0 0], a tie is [0 1 0]. I want to predict what a customer buys if they spend a certain ...
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How to train CNN for steering prediction without central camera?

I have a dataset with 32000 stereo pairs. For each pair I have steering angle label. For my robot I need CNN, which can predict steering wheel angle. But how to train it without central camera? I ...
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Is it a good idea to train Neural Network for classification on dataset where each document has a different class i.e. no class is repeated again?

My goal is to build a recommendation model for which I want to use Neural Network (LSTM). The user will give some input keywords and the model should return the suggestions (classes) based on ...
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BERT has a non deterministic behaviour

I am using the BERT implementation in https://github.com/google-research/bert for feature extracting and I have noticed a weird behaviour which I was not expecting: if I execute the program twice on ...
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Optimal architecture of neural networks for classification of samples with both text features and other features

Question: What is (from your experience) the most optimal architecture for a neural network for binary classification when the feature space is a mix of text and contextual features? Background: The ...
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LSTM Bounded Forecast Time Series

Can anyone please provide a logical explanation as to why an LSTM produces a 'bounded' forecast when predicting over unseen time series data? This behaviour does not seem to occur when using a MLP. ...
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Algorithm for sequences classification

I want to ask wich algorithm can I use to do a sequences classification , knowing that I have two classes (positive /negative), but training is done using data from one class only (positive). Thank ...
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1answer
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Time Series - Models seem to not learn

I am doing my undergrad Dissertation on time series prediction, and use various models (linear /ridge regression, AR(2), Random Forest, SVR, and 4 variations of Neural Networks) to try and 'predict' (...
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Deep learning theory: why are hidden layers necessary?

For this question, I’ll refer to the popular YouTube video by 3Blue1Brown on deep learning applied to recognition of written numbers: https://www.youtube.com/watch?v=aircAruvnKk The video describes ...
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Neural network with different input shapes

I'm currently designing the architecture of a neural network for the colorization of grayscale images. Later on it should be able to colorize images with different sizes and different aspect ratios. I ...
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Anyone have fruit disease dataset? [closed]

I am doing a project on fruit disease recognition and classification. Anyone have an existing dataset of fruit diseases? Can you help me to find one?
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How to feed a table per timestamp to LSTM neural network?

I have a time-series dataframe like this ...
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1answer
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What does it mean to be “stable to deformation”?

In the context of image classification, what does it mean to be stable to deformation? Say I were trying to classify digits, what would the difference be between an operation that is stable vs ...
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neural network to match image

I am pretty new to neural networks and I would appreciate some guidance... maybe books or articles on the following topic: I am an airfoil designer. At fixed flow conditions, the pressure on the ...
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1answer
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Maximize Precision Deep Learning

For some binary image classification problems having close to 100% precision is super important and recall is much less important. What are best practices for maximizing precision? Setting the ...
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Which accuracies to report in this case?

I am new to ML research and to writing ML paper. An ML research project resulted in a family of algorithms $A_i$. These algorithms transform certain type of data. This data is fed into a neural ...
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Neural Net Accuracy: Test Set vs Real World Data

Neural Net accuracy is high on test set but low on new real world image examples. Looking for advice regarding what generally causes this scenario and how to fix it. Sampling basis? Training/test ...
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Text Dataset Help: Need to figure out how to get the publication date of a text

I am a highschool student working on a science fair project in which me and a friend plan to use a neural network for a classification problem. In this case the thing classified will be text and the ...
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Padding sequences for neural sequence models (RNNs)

I am padding sequences for a GRU based classifier that I am building in Keras. I'm wondering if there's any accepted best practice for padding the leading or trailing side of the sequence. E.g. <...
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Same classification given for neural network regardless of the input

I am using the MNIST classification tutorials on the TensorFlow website to create my own classification program to predict a footballers value using the FIFA 19 dataset. However, when I run my program,...
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Problems with class embedding in keras

I am doing a toy example with mushroom dataset to learn class embedding with keras: I am trying to embed a single feature: ...
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is it possible determine a best neural network classifier by considering only accuracy

Below, you have the accuracy plots for training and testing set for 6 different neural networks. is it possible to say, which of the following neural network classifier is better?. Having this little ...
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Is it possible to make a 'forked path' neural network?

I want to make a network, specifically a CNN for image recognition, that takes an input, processes it the same way for several layers, and then at some point splits before coming to two different ...
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How to calculate receptive field size for -ception model?

I have a full convolution model like this: ...
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How to handle maxpool layer backpropagation with recurring max values in same position

Say I have a layer a: 3 4 2 1 5 0 8 6 4 The maxpool using 2x2 filter is: <...
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Backpropagation implementation help

I'm trying to implement Nokland's Direct Feedback Alignment in Python following his paper. Here's my implementation so far: ...
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1answer
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ValueError: Tensor Tensor(“activation_5/Softmax:0”, shape=(?, 2), dtype=float32) is not an element of this graph

There seem to be an issue with predicting using my keras model. I had trained it using the following keras code: ...
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50 views

People detection methods

In my problem I want to distinguish people from other shapes in images e.g I want to accurately know how many people are in specific region of image (at least for small number of people, for crowded ...