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Questions tagged [model-request]

Use this tag when you're looking for machine learning models that could be used to solve your specific problem.

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Is there a model for facial detection based on an infrared camera?

I need an AI model for facial detection based on an infrared camera Is there an existing model for this with per-trained weights? Does this model work well when the lighting conditions may change ...
errolflynn's user avatar
0 votes
1 answer
54 views

What ML/DL algorithms for frequency spectrum pattern classifications?

I have a set of known frequency spectrum data for this set of chemical compounds. Then the unknown Y is the mixture of some of these compounds. The task is to determine what compounds are in this ...
David293836's user avatar
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0 answers
15 views

Which kind of model should I be looking at if I'm trying to estimate volume for 3D objects?

I'm trying to navigate the world of LLMs and understand which classes of models are ideal for which use case. So I'm wondering, for problems related to 3D volume estimation, which class of models are ...
blueberryfields's user avatar
1 vote
0 answers
31 views

Is there some neural network that implements Least Squares?

I would like to build supervised NN that gets a matrix $A$ and vector $b$ as inputs and returns $x$ as a close result of the Least Squares algorithm for $Ax=b$. I looked for so works in the field and ...
ChaosPredictor's user avatar
2 votes
1 answer
60 views

Is there any interpretation method suitable for CNNs which do regression tasks?

I mainly tackle regression problems by CNNs, and want to find a reliable method to calculate the heatmaps for NN's results. However, I find almost all interpretation methods including CAM is used for ...
minghuisvn's user avatar
1 vote
1 answer
811 views

Are there Explainable GNN methods for node regression tasks?

I am wondering if there are gnn explainable methods for a regression task (e.g., traffic forecasting) where nodes have numerical features and the predicted output is a numerical value. Most of ...
Achiles Br's user avatar
1 vote
1 answer
33 views

What models/algorithms besides variational autoencoders can I use to transform a discrete input into a differentiable latent space?

Let's say I have a discrete input and want to transform it into a differentiable latent space. What models/algorithms besides variational autoencoders can I use?
postnubilaphoebus's user avatar
0 votes
1 answer
35 views

Is there any work done on topic agnostic binary topic classification?

In the recent preprint paper Tree-based Focused Web Crawling with Reinforcement Learning a new model is introduced to classify web pages called KwBiLSTM. The input to this model is a featurized ...
Kroshtan's user avatar
  • 249
0 votes
1 answer
43 views

Image classifier model which predicts objects and it's relevant areas with a combination of words

I have experience with image classification models such as CNN and Vision Transformers but this time I want to try a new thing (For me). First please check the below image to understand what I want ...
albert's user avatar
  • 3
0 votes
1 answer
45 views

How is it possible to detect anomalies in batches of 2 minutes of web access logs?

I have data coming from web access logs in the following form: ...
Kosmylo's user avatar
  • 161
2 votes
1 answer
127 views

Can the output layer be connected to multiple layers?

Normaly, the output layer is only connected to the second last layer. Is there any model that the output layer is connected to multiple layers (For example, the second last layer AND the layer before ...
kaka's user avatar
  • 21
1 vote
1 answer
58 views

Make an NN utilize other NNs as part of its decision process

Suppose I have a NN that learns to predict the time it takes a robot to move between two jobs. That's three inputs (for starters): robot, job A, job B. Not all robots travel at the same speed, and ...
Brannon's user avatar
  • 111
2 votes
1 answer
163 views

How can I generalize a machine learning model to multiple curves?

I have a family of convergence curves as you can see in the image below: I would like to train a model that fits reasonably well to all the curves at the same time in my dataset. Is it possible? Do ...
giovasbr's user avatar
0 votes
0 answers
20 views

Which existing model could be used for wind speed and direction prediction?

I am trying to predict the wind speed and wind direction in a graph network for a geographical area. The dataset includes the start and end nodes, the distance between them, and wind speed and ...
bsha's user avatar
  • 1
1 vote
1 answer
214 views

ML model to predict timeouts

I am new to ML and am trying to build a model to predict timeouts for a website. The website is being monitored once a minute and the data consists of a timestamp and the response time in seconds. E.g....
gwolter's user avatar
  • 11
2 votes
1 answer
86 views

How might AI analyze abusive discussion using natural language grammar?

Opening thoughts This does not only apply to SE comments, but the idea in general. This is not a Question for Linguistics.SE; those Questions might come later, after AI analysis. Example Linguistics ...
Jesse's user avatar
  • 123
1 vote
1 answer
940 views

Which approach can I use to generate forged signatures from real ones?

I am in internship period and I'm working on a signature verification problem. This process needs real and forged signatures. All I have are the real signatures (like 30 signatures per person), and I ...
Mohamed Yahyaoui's user avatar
1 vote
2 answers
3k views

Are there some known neural networks that, given an input image, can generate a similar image, with the same topic?

Are there some known neural networks that, given an input image, can generate a similar image, with the same topic? Example: input = a photo of a cat on a green table, output = a generated photo of ...
logijaz's user avatar
  • 49
0 votes
1 answer
87 views

Is there an approach where the output of one neural network is used to choose the next neural network?

I'd like to design a deep learning architecture in which the output of a primary neural network $M_{\theta}$ determines which neural network $N^i_{\alpha}$ in a set of secondary networks $\mathcal{N}$ ...
Wowee's user avatar
  • 1
0 votes
0 answers
173 views

Is it effective to use deep learning method to produce a 1D signal as output from a 2D image as input?

I have a 1D signal that will produce a 2D image after some image processing algorithm. Would it be possible and effective to use deep learning method to reproduce the 1D signal if I have the 2D image ...
GordonJun's user avatar
1 vote
1 answer
907 views

How to train an ML model to convert the given lyrics into a song by a particular singer?

I am interested in training a machine algorithm to convert the lyrics I give into a song by a particular singer. My language is non-English (south Indian) The songs are mostly monophonic (very few ...
bababee's user avatar
  • 15
2 votes
0 answers
118 views

Non-face "deepfakes" in videos

Instead of changing faces (like James Bond to Putin) what if, given sufficient training data, I wanted to: Remove or add some windows from a brick house? Convert a glass of red wine to a glass of ...
Derek Fulton's user avatar
1 vote
0 answers
14 views

What is the best way to train a text-based regressor model?

I want to build a deep learning model that can predict a continuous value (LogP in this case) given text inputs (SMILES notations in this case), the dataset is as illustrated below. SMILES notations ...
mac179's user avatar
  • 121
1 vote
0 answers
487 views

What is the fastest multi-human pose estimation model?

I am trying to find an accurate and fast multi-person human pose estimation that I can train on with custom data. I have been searching for a little while and I may not be up-to-date on the newest ...
Kevin's user avatar
  • 133
2 votes
1 answer
40 views

What type of neural network do you need if you want to detect an action or dynamic pattern instead of a static pattern?

Let's say that you want to detect if a man is running, walking, or dancing instead of just detecting a man still. What type of neural networks will you use for this purpose?
S. Feunmajer's user avatar
4 votes
2 answers
724 views

What algorithms are used in Artificial General Intelligence research?

I've read on wiki that already in 2017 there were over 40 institutions researching AGI, and I wonder what type of algorithms are being studied and developed in this field. For example, for comparison ...
GKozinski's user avatar
  • 1,260
1 vote
0 answers
103 views

What is the best way to train neural network with imbalanced mixed data (images and structured data)?

I have structured data and image data to solve a regression problem. One sample of structured data can be related to N images. If I use only structured data, I get decent performance, but not enough ...
thereiter's user avatar
2 votes
0 answers
53 views

Model for direct audio-to-audio speech re-encoding

There are many resources available for text-to-audio (or vice versa) synthesis, for example Google's 'Wavenet'. These tools do not allow the finer degree of control that may be required regarding the ...
NeverWasMyRealName's user avatar
0 votes
1 answer
125 views

What type of ANN architecture to choose?

I have $N$ number of teachers each of which has an input feature vector ($25$ dimensional) consisting of positive numerical values for different quality of aspects (for example: lecturing ability, ...
user3489173's user avatar
4 votes
2 answers
222 views

Which neural network can I use to solve this constrained optimisation problem?

Let $\mathcal{S}$ be the training data set, where each input $u^i \in \mathcal{S}$ has $d$ features. I want to design an ANN so that the cost function below is minimized (the sum of the square of ...
user3489173's user avatar
1 vote
1 answer
63 views

What kind of neural network should I build to classify each instance of a time series sequence?

Let's say I have the time-series dataset below-left. I would like to train a model in such a way that, if I feed the model with an input like the test sequence below, it should be able to classify ...
bbasaran's user avatar
  • 133
1 vote
1 answer
66 views

What would be the state of the art image captioning deep learning model?

I saw a couple of architectures, like CNN-LSTM, with and without attention model, use of Glove vector, self-critical models, etc. I am overwhelmed looking at different notebooks and architectures, ...
Joe's user avatar
  • 113
0 votes
0 answers
37 views

Can RNNs be used to classify these time series into two classes?

My task is to classify into two classes the time series like these shown in the figure. The figure shows one class on the left sub-figure and second one on the right. The series are shown in pairs ...
Marek's user avatar
  • 9
0 votes
2 answers
135 views

Should I use U-net to label keys in a keyboard image?

This is a 600*800 image. Which algorithm/model should I use to get an image like the one below, in which each key is detected and labeled by a rectangle? I guess this is some kind of a segmentation ...
JJJohn's user avatar
  • 221
1 vote
1 answer
124 views

Which approach should I use to classify points above and below a sine function $y(x) = A + B \sin(Cx)$?

In a linear regression problem, a line can divide a data set into two categories. So, basically, points above the line belong to category 1, and points below the ...
Quadro's user avatar
  • 111
1 vote
0 answers
64 views

Setting up a deep learning architecture for multi-dimensional data

The input data is thousands, millions of 4x1000 matrices. Each row consists of 3 small natural numbers (1000 combinations) and a corresponding real number between 0 and 1. The output is a 1x1000 ...
deviant_sci's user avatar
1 vote
1 answer
98 views

Is it possible to make a neural network to solve this "reaction time test"?

I'm thinking about writing an essay on the comparison between the human nervous system (reaction time) and a neural network that does the same reaction time test. I am very new in this area, so I was ...
Rutvik Karupothula's user avatar
1 vote
1 answer
315 views

Is there a graph neural network algorithm that can deal with a different number of input and output nodes?

I am new to graph neural networks and their applications. I have an input graph $G = \{V, E\}$ and an output graph $G' = \{V', E'\}$ where the number of nodes $V$ and $V'$ are different. I am trying ...
shunyo's user avatar
  • 133
1 vote
0 answers
298 views

Object detection approaches without anchors and NMS

The Context From all of the problems I have worked with in computer vision, the most challenging one is the object detection. This is not because the problem itself is complex to understand or bad ...
JVGD's user avatar
  • 1,108
2 votes
1 answer
213 views

Which neural network can approximate the function $y = x^2 + b$?

I am new to ANN. I am trying out several 'simple' algorithms to see what ANN can (or cannot) be used for and how. I played around with Conv2d once and had it recognize images successfully. Now I am ...
Mike de Klerk's user avatar
2 votes
1 answer
94 views

How many ways are there to perform image segmentation?

I'm new in Artificial Intelligence and I want to do image segmentation. Searching I have found these ways Digital image processing (I have read it in this book: Digital Image Processing, 4th edition)...
VansFannel's user avatar
11 votes
2 answers
3k views

Is there any existing attempt to create a deep learning model which extracts vector paths from bitmaps?

I need an algorithm to trace simple bitmaps, which only contain paths with a given stroke width. Is there any existing attempt to create a deep learning model which extracts vector paths from bitmaps? ...
arthur.sw's user avatar
  • 161
0 votes
1 answer
1k views

Neural networks for sports betting

I want to design a neural network that can be used for predicting sports scores for betting, specifically for American football. What I’d like to do is create a kind of profile for each game based on ...
SuperCodeBrah's user avatar
2 votes
0 answers
323 views

Is there a neural network that can output a unit vector that is parallel to the input vector?

I'm wondering if there is a NN that can achieve the following task: Output a unit vector that is parallel to the input vector. i.e., input a vector $\mathbf{v}\in\mathbb{R}^d$, output $\mathbf{v}/\|\...
Tony B's user avatar
  • 129
4 votes
0 answers
55 views

What are some neural network models that can use auxiliary info during training for image segmentation?

What are some deep learning models that can use supplementary information other than RGB channels for image segmentation? For example, imagine a poorly shot image of a river (blue) that shows a gap, ...
Nanako Honda's user avatar
1 vote
1 answer
44 views

Is any classifier not subject (or less susceptible) to fooling?

Is any classifier not subject to fooling as in here? My question is related to this, but not an exact duplicate. What I wanted to ask is that any classifiers inherently do not subject (or less prone) ...
user559678's user avatar
5 votes
1 answer
5k views

Which neural network can count the number of objects in an image?

I'm looking for a neural network architecture that excels in counting objects. For example, CNN that can output the number of balls (or any other object) in a given image. I already found articles ...
ron653's user avatar
  • 83
2 votes
1 answer
136 views

Is there any research on models that make predictions by also taking into account the previous predictions?

With the recent revelation of severe limitations in some AI domains, such as self-driving cars, I notice that neural networks behave with the same sort of errors as in simpler models, i.e. they may be ...
user4779's user avatar
  • 203
3 votes
2 answers
2k views

Which model should I use to find (only) the object location (in terms of coordinates) in an image?

I am generating images that consist of points, where the object's location is where the most overlap of points occurs. In this example, the object location is $(25, 51)$. I am trying to train a model ...
scooter's user avatar
  • 31
2 votes
2 answers
722 views

Which neural network should I use to approximate a specific but unknown function?

We have convolutional neural networks and recurrent neural networks for analyzing, respectively, images and sequential data. Now, suppose I want to approximate the unknown function $f(x,y) = \sin(2\pi ...
timudk's user avatar
  • 31