Questions tagged [machine-learning]

For questions about machine learning (ML) concepts, mathematics, research, general practice, and quality control. Machine learning is a sub-field of the broader field of artificial intelligence (AI).

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22 views

Unique game problem (ML, DP, PP etc)

Looking for a solution to my below game problem. I believe it to require some sort of reinforcement learning, dynamic programming, or probabilistic programming solution, but am unsure... This is my ...
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28 views

Does this hyperparameter optimisation approach yield the optimal hyperparameters?

Say I have a ML model which is not very costly to train. It has around say 5 hyperparameters. One way to select best hyperparameters would be to keep all the other hyperparamaters fixed and train ...
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2answers
56 views

What is the difference between a machine learning engineer and deep learning engineer?

What is the difference between a Machine Learning Engineer and Deep Learning Engineer and an AI developer? What would be their daily tasks at the office?
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1answer
18 views

How to rescale data to its original range after MinMaxScaler?

I'm using sklearn's MinMaxScaler in order to scale my data down. However, it would be nice to be able to rescale it back to its original range. Is there any way I ...
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1answer
128 views

How to stay a up-to-date researcher in ML/RL community?

As a student who wants to work on machine learning, I would like to know how it is possible to start my studies and how to follow it to stay up-to-date. For example, I am willing to work on RL and MAB ...
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2answers
134 views

Can a computer conclude following philosophical concepts from a story?

Say you have to enter a story to a computer. Now the computer has to identify the philosophical concept on which the story is based, say: Was it a "self-fulfilling prophecy"? Was it an example of "...
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53 views

What can be inferred about the training data from a trained neural network?

Suppose we trained a neural network on some training set that we call $X$. Given the neural network and the method of training(algorithm, hyperparameters etc.) can we infer anything about $X$. Now, ...
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46 views

What is a conditional random field?

I new in machine learning, especially in Conditional Random Fields (CRF). I have read several articles and papers and in there is always associated with HMM and sequences classification. I don't ...
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2answers
36 views

How to enforce covariance-matrix output as part of the last layer of a Policy Network?

I have a continuous state space, and a continuous action space. The way I understand it, I can build a policy network which takes as input a continuous state vector and outputs both mean vector and ...
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1answer
64 views

Which models accept numerical parameters and produce a numerical output?

I need a model that will take in a few numerical parameters, and give back a numerical answer (Context: predicting a slope based on environmental factors without having to actually take measurements ...
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57 views

How do I determine the generalisation ability of a neural network?

I am trying to ascertain if my neural network is able to generalize or if it’s simply using memory/overfitting to solve a task. I would like my model to generalise. Currently, I train the neural ...
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1answer
42 views

How to recognize with just name and last name if the person is a political exposed person

First than all, I am not sure if this questions is more about Machine Learning, or if its Artificial Intelligence, if not, just let me know I will delete it. At my company we need to create a ...
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10 views

Scikit-Learn: monotoneous quantile estimation

I would like to implement various AI-estimators for quantile estimation for a regression problem. It would be necessary to have non-crossing quantiles, that is larger quantiles would correspond to ...
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4answers
9k views

Are neural networks prone to catastrophic forgetting?

Imagine you show a neural network a picture of a lion 100 times and label with "dangerous", so it learns that lions are dangerous. Now imagine that previously you have shown it millions of images of ...
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60 views

Is this a classification problem?

I’m not really sure which machine learning approach is best for my problem at hand. I work in an engineering company that designs and builds different kinds of ships. In my particular job, I collect ...
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51 views

How can I develop this ML/AI system that I want to use in my new mobile app?

I have an idea for a new mobile app. Here is what I want to accomplish using AI; I want to get an image (png format), (maybe just byte data too), from my application (I'm developing with Unity3D/C#), ...
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17 views

Models to extract Causal Relationship between entities in a document using Natural Language Processing techniques

I am looking to extract causal relations between entities like Drug and Adverse Effect in a document. Are there any proven NLP or AI techniques to handle the same. Also are there ways to handle cases ...
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1answer
36 views

What is “dense” in DensePose?

I've recently come across an amazing work for human pose estimation: DensePose: Dense Human Pose Estimation In The Wild by Facebook. In this work, they have tackled the task of dense human pose ...
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37 views

How should I build an AI that quickly detects falling game assets on screen?

I want to build an AI that plays a simple android game. The game is just a one at a time object falling, some times at an angle. The AI needs to recognize the object and to decide whether to swipe ...
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2answers
129 views

Can neuroevolution be combined with gradient descent?

Is there any precedent for using a neuroevolution algorithm, like NEAT, as a way of getting to an initialization of weights for a network that can then be fine-tuned with gradient descent and back-...
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1answer
39 views

Which loss function should I use for binary classification?

I plan to create a neural network using Python, Keras and TensorFlow. All the tutorials I have seen so far are concerned with image recognition. However, the goal of my program would be to take in 10+ ...
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1answer
42 views

How are the parameters of the Bernoulli distribution learned?

In the paper Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask, they learn a mask for the network by setting up the mask parameters as $M_i = Bern(\sigma(v_i))$. Where $M$ is the ...
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23 views

What are the advantages of time-varying graph CNNs compared to fixed graph?

As I wrote in the title, what are the advantages of time-varying graph CNNs compared to fixed graph? For example, in CORA, which is a graph of citation relations of papers frequently used in graph CNN,...
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1answer
29 views

Training on daily new data

I would like to know if it was possible to train a neural network on daily new data, let me explain. Let's say you have daily data from 2010 to 2019, you train your NN on all of it, but from now on, ...
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36 views

Why do neural networks have bias units?

Why do neural networks have bias units? Why is it sometimes okay to opt them out?
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2answers
60 views

How can I train a neural network to give probability of a random event?

Let's say I have an adjustable loaded die, and I want to train a neural network to give me the probability of each face, depending on the settings of the loaded die. I can't mesure its performance on ...
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8 views

Reducing the Number of Training Samples for collaborative filtering in recommender systems

I have the following problem: I am doing some research on the accuracy of recommender algorithms that are mostly used nowadays. So, one way to measure their performance is by checking how well they ...
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1answer
43 views

Is there a neural network method for time-varying directed graphs?

I want to study NN for time-varying directed graphs. However, as this field has developed relatively recently, it is difficult to find new ways. So the question is, is there any NN that can handle ...
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0answers
12 views

Product Configuration based on user selection of features and other requirements

Is this a scenario that would work well for a ML/Pattern Recognition Model or would it be easier/faster to just filter from a large DB. I am looking to create a system that will allow users to ...
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2answers
52 views

How to compute the number of centroids for K-means clustering algorithm given minimal distance?

I need to cluster my points into unknown number of clusters, given the minimal Euclidean distance R between the two clusters. Any two clusters that are closer than this minimal distance should be ...
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1answer
29 views

How does the CTC loss work?

I am trying to implement CTC loss in Tensorflow, but their documentation is pretty limited. So I am not sure how to approach the problem. I found a good example in Theano: https://github.com/...
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1answer
39 views

Examples of time-varying graph-structured data in real world

I'm looking for examples of time-varying graph-structured data for time-varying graph CNNs. First, I came up with the idea of infection network. Is there anything more? If possible, I want data that ...
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0answers
16 views

How is the bias caused by a max pooling layer overcome?

I have constructed a CNN that utilises max pooling layers. I have found with these layers that, should I remove them, my network performs ideally with every output and gradient at each layer having a ...
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26 views

Which model to use when selecting objects of interest?

I have a set of polygons for each image. Those polygons consist of four $x$ and $y$ coordinates. For each image, I need to extract the ones of interest. This could be formulated as an Image ...
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15 views

What parameters can be tweaked to avoid a generator or discriminator loss collapsing to zero when training a DC-GAN?

Sometimes when I am training a DC-GAN on an image dataset, similar to the DC-GAN PyTorch example (https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html), either the Generator or ...
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1answer
22 views

In NN, as iterations of Gradient descent increases, the accuracy of Test/CV set decreases. how can i resolve this?

As mentioned in the title I'm using 300 Dataset example with 500 feature as an input. As I'm training the dataset, I found something peculiar. Please look at the data shown below. Iteration 5000 |...
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14 views

Random graph as input in geometric deep learning on time-varying graph

I want to create a framework that allows GDL to be applied to time-varying graphs. I came up with the Erdos-renyi model as an example of a time-varying graphs. GDL for graphs takes node information ...
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1answer
31 views

How are exploding numbers in a forward pass of a CNN combated?

Take AlexNet for example: In this case, only the activation function ReLU is used. Due to the fact ReLU cannot be saturated, it instead explodes, like in the following example: Say I have a weight ...
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23 views

Where could I find information on the learning methods used in Neurogrid?

I have been searching for more than one week which learning methods were used in Neurogrid. But I only found descriptions of its architecture (chips, circuits, analog and/or digital components, ...
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23 views

I am looking for research related to the use of AI and ML in car, aeronautics manufacturing design and safety

I am specifically interested in the topic of edge cases. I have the presentation Edge Cases and Autonomous Vehicle Safety as a starting point, in particular on page 6: Machine Learning (inductive ...
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1answer
64 views

Why can a fully convolutional network accept images of any size?

On this article, it says that: The UNET was developed by Olaf Ronneberger et al. for Bio Medical Image Segmentation. The architecture contains two paths. First path is the contraction path (...
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29 views

What are the benefits of using the state information that maintains the graph structure?

When you applying a graph structured data to the graph convolution network, what are the benefits of using the state information that maintains the graph structure?
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1answer
41 views

How do I know if my dataset is ready for a machine learning model?

I am new in this area of Machine Learning and Neural Networks. Currently, I'm taking some courses on Udemy and reading a book about it, but I still have one big question regarding data pre-processing. ...
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1answer
45 views

What is the purpose and benefit of applying CNN to a graph?

I'm new to the graph convolution network. I wonder what is the main purpose of applying data with graph structure to CNN?
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26 views

Is predicting day of week straight forward?

I am using python and Xgboost. I have features: activity and location and time stamps of when the activity occurred. I want to predict day of week. Is this straight forward, ie y=day of week, X={...
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1answer
38 views

How do I classify strings with possibly no meaning?

I am quite new to text classification. Using EAST text detection model, I get multiple strings that aren't words and most often have no meaning. For example, IDs, brand names, etc. I would like to ...
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1answer
15 views

Sensitivity of neural network to inputs

I am trying to build intution regarding neural networks and their working and this is a question, I am interested in: I understand that we normalise inputs. The reason this is done is to capture the ...
2
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1answer
141 views

How do I find the distance?

I am looking for solving this problem with training a deep learning-based classifier or image processing techniques. ps. I exactly do not need to know how much is distance, I only need to know whether ...
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2answers
23 views

How can I use 1-channel images as input to a CNN?

I need to develop a convolutional neural network whose inputs are 1-channel images, but I dont know how to do it, given that most libraries use 3 channel images. Should I convert my images to RGB? Is ...
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33 views

How to approach a problem with infinite solutions

Think Angry Birds kind of game. You need to hit a target at some point by adjusting angle and power. There is infinite number of parabolas that will solve this problem. My problem is not exactly that ...