All Questions
38 questions
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65
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How to train a model to make predictions for larger sequences than those in the dataset?
I'm working on a project where I need my model to predict a sequence of n 3x3 matrices given an input sequence of n 3x3 matrices ...
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0
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26
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Why isn't my CNN-RNN model learning despite the CNN performing well?
I'm working on a model that combines a CNN with an LSTM to process sequences of spectrograms and make per-time-step predictions. The CNN alone performs well on the task, but after adding an LSTM for ...
1
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1
answer
72
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Is image machine translation done in two steps?
Suppose I have images of hand-written Japanese text. If I want to translate those images, would my ML algorithm be a 2-step model (for example, a CNN to convert the image into Japanese characters/...
CommunityBot
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1
answer
94
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What underlying network is typically meant with ResNET?
When people talk about a ResNet architecture, they are talking about a neural network architecture with skip connections. But what basis network are they typically referring to? Feedforward-networks ...
CommunityBot
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2
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1
answer
64
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Distinguishing between the fundamental structures of the convolutional neural network and the recurrent neural network: hierarchical vs sequential
I'm trying to distinguish between the fundamental structures of the convolutional neural network and the recurrent neural network. Convolutional neural networks build a hierarchical model from the ...
- 170
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0
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70
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Is my 1D signal using CNN & RNN regression reasonable?
I want to know if my impact-echo signals are proper with CNN or RNN regression model.
I got some simulated signal, as following shows.
In previous research, people mostly consider frequency or even ...
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1
answer
44
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Temporally Non-Aware RNN
I am trying to classify whether or not a specific object is in panoramic photos. The issue is, a panoramic photo can be any width, so the input to my neural network can't be fixed in that dimension.
I'...
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1
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54
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Neural network for recognizing ship types based on location series
I am building a neural network for recognizing ship types based on a 1000-long series of location data (latitude-longitude, normalized to account for different km/longitude° metrics, so that vector ...
CommunityBot
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13
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5
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8k
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What is the fundamental difference between CNN and RNN?
What is the fundamental difference between convolutional neural networks and recurrent neural networks? Where are they applied?
- 2,925
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1
answer
44
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How to deal with varying number of input images?
Im trying to use Deep-Learning to recognize breast cancer on Mammography Images. But in the dataset every patient has a different (1-4) number of images taken. How can i deal with that? Generally i ...
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1
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1k
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What is the difference between CNN-LSTM and RNN?
I'm starting to study RNN for a project of video prediction, but I encounter these CNN-LSTM models. Initially, I thought that is another name for RNN, but I think I get it wrong. Since I'm a beginner ...
- 41.4k
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35
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What could be causing the poor performance (MSE) of a dense neural network on a real time-series dataset?
I am trying to understand time series analysis and actually I am following the course "Sequences, Time Series and Prediction" in Coursera. The course is based on a synthetic dataset, ...
- 41.4k
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0
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51
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Does it make sense to compare images (samples) with words (features)?
Consider the following paragraphs from the introduction of the chapter named Recurrent Neural Networks from the textbook titled Dive into Deep Learning
So far we encountered two types of data: ...
- 41.4k
3
votes
1
answer
824
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In a Temporal Convolutional Network, how is the receptive field different from the input size?
I'm playing around with TCN's lately and I don't understand one thing. How is the receptive field different from the input size?
I think that the receptive field is the time window that TCN considers ...
- 208
3
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1
answer
420
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Why would we use attention in convolutional neural networks, and how would we apply it?
Attention has been used widely in recurrent networks to weight feature representations learned by the model. This is not a trivial task since recurrent networks have a hidden state that captures ...
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1
answer
32
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Is reconciling shape discrepancies the only purpose of padding?
Padding is a technique used in some of the domains of artificial intelligence.
Data is generally available in different shapes. But in order to pass the data as input to a model in deep learning, the ...
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0
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95
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Feeding the output back to input in 3D CNN model
I am currently designing a Model which takes Input 3D Grid and Model Output at $t-1$. The model figure is described below
I have two thoughts in training the model for above situation.
Feed output $...
- 3,990
-1
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0
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25
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Document clustering from ordered pages list
I have a series of ordered pdf pages which own to different documents. Let me give you an example:
Pages: 1 2 3 4 5 6
True Pages: 1 2 | 1 2 3 4
So I have like six ordered pages, two of which from ...
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0
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1
answer
258
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Are 1D CNNs really the appropriate model for human activity recognition?
This article on human activity recognition states that 1D convolutional neural networks work the best on the classification of human activities using data from the accelerometer. But I think that ...
- 350
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1
answer
102
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Must all CNNs and RNNs not have a fully connected layer in order to be considered as such?
In the paper Wrist-worn blood pressure tracking in healthy free-living individuals using neural networks, the authors talk about a combination of feed-forward and recurrent layers, as if FC layers ...
- 41.4k
1
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0
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325
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How do I infer exploding or vanishing gradients in Keras?
It may already be obvious that I am just a practitioner and just a beginner to Deep Learning. I am still figuring out lots of "WHY"s and "HOW"s of DL.
So, for example, if I train a ...
3
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1
answer
84
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Is there a common way to build a neural network that seeks to extract spatial and temporal information simultaneously?
Is there a common way to build a neural network that seeks to extract spatial and temporal information simultaneously? Is there an agreed up protocol on how to extract this information?
What ...
- 1,168
1
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1
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75
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What's a good neural network for this problem?
I am very new to the field of AI so please bear with me.
Say there is a dice with three sides, -1,0 and 1, and I want to predict which side it lands on (so only one output is needed I guess). The ...
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47
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How can one be sure that a particular neural network architecture would work?
Traditionally, when working with tabular data, one can be sure(or at least know) that a model works because the included features could explain a target variable, say "Price of a ticket" ...
- 41.4k
2
votes
1
answer
187
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Why are RNNs used in some computer vision problems?
I am learning computer vision. When I was going through implementations of various computer vision projects, some OCR problems used GRU or LSTM, while some did not. I understand that RNNs are used ...
- 41.4k
3
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1
answer
88
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From an implementation point of view, what are the main differences between an RNN and a CNN?
I understand that in general an RNN is good for time series data and a CNN image data, and have noticed many blogs explaining the fundamental differences in the models.
As a beginner in machine ...
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2
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135
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Do convolutional neural networks also have recurrent connections? [duplicate]
I asked my self this simple question while reading "Comment Abuse Classification with Deep Learning" by Chu and Jue. Indeed, they say at the end of the that
It is clear that RNNs, specifically ...
- 41.4k
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0
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38
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Deep learning techniques with time-fixed, time-dependent and imaging data
I have a question about the use of deep learning techniques with time-fixed features and images (setting 1) and time-dependent features (setting 2). (I am pretty new to the deep learning world so ...
- 1,283
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0
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22
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How do CNNs or RNNs "stack the feature of nodes by a specific order"?
I am trying to understand the following statement taken from the paper Graph Neural Networks: A Review of Methods and Applications (2019).
Standard neural networks like CNNs and RNNs cannot handle ...
- 41.4k
10
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4
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3k
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What are the models that have the potential to replace neural networks in the near future?
Are there possible models that have the potential to replace neural networks in the near future?
And do we even need that? What is the worst thing about using neural networks in terms of efficiency?
CommunityBot
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3
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0
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41
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How to predict an event (or action) based on a window of time-series measurements?
I have an input vector $X$, which contains a series of measurements within a period, e.g. 100 measurements in 1 sec. The goal is to predict an event, let's say, moving forward, backward or static.
I ...
- 41.4k
1
vote
1
answer
113
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Are there names for neural networks with a well-defined layer or neuron characteristics?
Are there names for neural networks with a well-defined layer or neuron characteristics?
For example, a matrix that has the same number of rows and columns is called a square matrix.
Is there an ...
- 41.4k
1
vote
1
answer
127
views
Convert input dataset given in hex addresses to int
I have created an LSTM Neural Network which take as input the following format in an .csv file
...
- 157
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0
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34
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Imposing contraints on sequence of image classifications
Are there example implementations of networks that apply constraints across sequences of image classifications where class labels are ordinal numbers? For example, to cause the output of a CNN to ...
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3
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0
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59
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Ideas on a network that can translate image differences into motor commands?
I'd like to design a network that gets two images (an image under construction, and an ideal image), and has to come up with an action vector for a simple motor command which would augment the image ...
- 275
1
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0
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25
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Literature on Sequence Regresssion
I have some rated time-sequential data and I would like to test if an ANN can learn a correlation between my measurements and ratings.
I suspect I could just try a CNN where 1 Dimension is time or an ...
- 153
1
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1
answer
106
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How to manage high numbers of input layer data points
Not sure if this is the correct forum, but I have been working with a large (non-image) dataset that will eventually be used to train a neural network. I have been puzzling over how to manage wide ...
- 166
2
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0
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50
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Recommendations on which architecture to use to guess appointment
I'm currently developping an application which allows psychologists to manage their schedule and budget. As a proof of concept, I would like to create an intelligent appointment service. There can be ...
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