Questions tagged [machine-learning]

For questions related to machine learning (ML), which is a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty (such as planning how to collect more data). ML is usually divided into supervised, unsupervised and reinforcement learning. Deep learning is a subfield of ML that uses deep artificial neural networks.

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Why do the authors of the T5 paper say that the "architectural changes are orthogonal to the experimental factors"?

Here's a quote from the T5 paper (T5 stands for "Text-to-Text Transfer Transformer") titled Exploring the Limits of Transfer Learning with a Unified Text-...
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Machine learning with raw data alone / or raw data with its statistics

My question is very general and it does not originate from a specific problem. Let's assume that, through experience, we have learned that some statistical property of a set of data is important in ...
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How do I prepare this 3D data for NN?

How do I prepare the info of 3D models to use with NN? For example, I have thousands of models with boxes similar to the ones in the image below. I can extract the vertices and their normals that make ...
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What are the different possible usages of the word "i.i.d" in machine learning?

The acronym "iid" stands for "independent and identically distributed". It is a property of a sequence of random variables. You can read here for more details. This question is ...
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Discrepancy of backpropagation formula between Andrew Ngs ML Course and those derived by neuralnetworksanddeeplearning.com

I'm currently working through Week 5 of Andrew Ngs Machine Learning course on Coursera, which goes through the backprop algorithm for basic neural networks. Whilst trying to derive the formulae he ...
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Is the inductive bias always a useful bias for generalisation?

Is it true that a bias is said to be inductive iff it is useful in generalising the data? Or does inductive bias can also refer to the assumptions that may cause a decrease in performance? Suppose I ...
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1answer
57 views

Different ways to calculate backpropagation derivatives, any difference?

I'm studying error backpropagation in neural networks. I am interested in why we use only one path on the computational graph to get the value of the derivative for a weight? I ask the question ...
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83 views

What is the formal definition for manifold in artificial intelligence?

We come across the word "manifold" in artificial intelligence, especially in the domains where learning is done based on data instances. What is the formal definition for manifold?
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When can we call a loss function "adaptive"?

A loss function is a measure of how bad our neural network is. We can decrease the loss by proper training. I came across the phrase "adaptive loss function" in several research papers. For ...
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Is it possible to train parameters engaged in more than two loss functions?

the images are from the paper titled Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification by Seokeon Choi et al. as you can see in the first picture p1 ...
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Why labeling facades?

In Pix2Pix by Isola et al. they translate images from different pairs of image categories to one another. While most other example applications for the algorithm make sense to me, I'm having ...
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Alternative chatbot service with export intent/entities function like DialogFlow? [closed]

I've been searching for a chatbot-building website with exportable intents/entities to a particular format (Spreadsheet, CSV, JSON, etc.). But the chatbots I have found so far like Flow.ai or ChatFuel ...
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Machine Learning Algorithm for OCR on full pages of text

I would like to build an OCR application. In. particular, I want my algorithm to scan entire pages of text in a specific niche language. I was therefore wondering if there are some algorithms that ...
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Get the name of a merchant from records

I have a bunch of bank transaction records from which I want to extract merchants' names. In a few subsets of these records, the structure of the string is the same within the subset with only the ...
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1answer
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What is the fundamental difference between the synthesis task and sampling task?

Among the list of tasks in machine learning, synthesis and sampling is one of the key task. Consider the following explanation regarding synthesis and sampling task from Chapter 5: Machine Learning ...
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1answer
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Can I always interpret features as random variables in machine learning safely?

Consider the following statements from Chapter 5: Machine Learning Basics from the book titled Deep Learning (by Aaron Courville et al.) Machine learning tasks are usually described in terms of how ...
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Residual Blocks - why do they work?

I've learnt that idea that the residual block was invented to solve the vanishing gradient problem due to the deep layer to layer multiplication. I understand that for example if I have 10 layers, and ...
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Are there any ideas/concepts from Hypothesis Testing that is used in Machine Learning?

A typical statistics book is usually divided into two parts: Hypothesis Testing and Estimator Theory. In Machine learning, I frequently see the application of ideas/concepts from estimator theory like ...
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How to properly report results for a medical image segmentation task?

Let’s consider a 2-class / binary segmentation problem where c=0 for background (healthy tissues) and c=1 for foreground (...
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52 views

What is the definition of "confidence interval" around a (complicated) function?

Consider the following excerpt from Chapter 5: Machine Learning Basics from the book titled Deep Learning (by Aaron Courville et al.) Machine learning is essentially a form of applied statistics with ...
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1answer
26 views

Best algorithms/approaches for data sets of binary (1/0) features

I am working with a dataset with about 400 features, all binary (1 or 0). What approach would you recommend? Data set is about 500k records.
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What is the borderline between unsupervised learning and regular algorithms?

Unsupervised learning using neural networks is clearly machine learning since it is utilising neural nets. However, some algorithms, k-means clustering, for example, are considered unsupervised ...
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Role of confidence or classification score in object detection mAP metrics

I know that mAP (mean Average Precision) is the common evaluation metric for the object detection tasks. It uses IoU (Intersection over Union) threshold such as mAP@0.5 to evaluate whether the ...
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How to get Attention Maps from Attention Gates in Attention UNET?

Contex I have Attention UNET for image segmentation. I use it for humans segmentation. Question Everything works fine. I want to get attention maps from my network, so I could see what my UNET is ...
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A comparison of Expert Systems and Machine Learning approaches in terms of run-time-efficiency and time/space complexity

For part of a paper I am writing on Clinical Decision Support Systems (computer-aided medical decision making, e.g. diagnosis, treatment), I am trying to compare Expert Systems with systems based on ...
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The results are not correct when predicting the future for a very long period of time with LSTM

I am currently using LSTM to try to predict future data in AirPassengers.csv. This is current code op my Colab (sorry for the comments are Japanese) https://colab.research.google.com/drive/...
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Is it possible to solve a linear programming problem using reinforcement learning? (DDPG algorithm)

I'm trying to solve a linear programming problem using reinforcement learning. The linear programming problem is: \begin{array}{ll} \text{maximize}_x & C* x \\ \text{subject to}& A*x \le b\\ ...
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Predict a part of the input based of the output

I'm working on a fun project where I have a dataset of input and output data, both having a fixed size of characters. I would like to predict a part of the input based on a known output as follows: $$...
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1answer
19 views

Selecting class weights for loss function

I have a machine learning task where I would like to weight losses based on the frequency of the categorical values appearing in the data. The binary solution can be seen below, but I'd like to know ...
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3answers
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Is there any domain in machine learning that solves a problem by using only analytical algorithms?

Most of the algorithms in machine learning I am aware of use datasets and learning happens in an iterative manner given some examples. The examples can also be understood as experience in the case of ...
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What does all the formula and pictures mean?

https://www.nature.com/articles/s41467-020-17419-7 I am a medical school graduate and I really want to learn AI/ML for computer-aided diagnosis. I was building a symptom checker and I found the ...
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43 views

Do other online/incremental algorithms not suffer from catastrophic forgetting?

All the literature I read seems to indicate catastrophic forgetting affects only neural networks. Do other online/incremental algorithms not suffer from catastrophic forgetting (for example, ...
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In what situation would you want to use NEAT over reinforcement learning?

NEAT is an evolutionary algorithm. When would you want to use NEAT over more traditional/common RL algorithms like PPO or SAC etc. What advantage does it give you?
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Can NeuralHash be used as a loss for an Autoencoder?

I've recently read about NeuralHash, and immediately thought that it might be used as a loss for an autoencoder. However, it only seems to preserve "structure" from what I've read, not ...
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Graph neural network - what level (node or link or graph) prediction should be used for my problem?

I posted this on cross-validated but did not get a response. Trying my luck here. Sorry if this is not recommended. I have an undirected graph with nodes separated within a specified distance, say d, ...
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How to pass variable length data as feature to a neural network?

I am working on building a model to classify the type of touch the user makes(Long Press, Left Swipe, Right swipe and so on). I have data with features that characterise the user's touch, like ...
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What is the significance behind having small kernel sizes over having one large kernel size that covers the entire input in a CNN?

I have hardly ever seen anyone cover the entire input image with a filter of the same dimensions. I was wondering why that is the case, and if the performance in say, an image detection application ...
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1answer
106 views

What is numerical stability?

I came across the phrase "numerical stability" several times. But almost in the same context. I encountered this word mostly in the analytical formula for batch normalization. $$y = \dfrac{...
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1answer
19 views

Data analysis before feeding to ML pipeline

I'm new to machine learning and I've been working through a dataset of ~3000 records with ~100 features. I've been hand rolling Python and R scripts to analyse the data. For example, plotting the ...
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Weight for Samples on SVM (Support Vector Machine)

There is a option sample_weight in fit(X[, y, sample_weight]) function (OneClassSVM, sklearn library). If I use the option ...
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Why do we transform feature vectors in attention modules for CNNs

If we have a set of feature maps with dimensions [B, C, H, W] (batch, channel, height, width), why do we transform our feature maps before we calculate their affinity/correlation in attention ...
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1answer
38 views

Attention mechanism: Why apply multiple different transformations to obtain query, key, value

I have two questions about the structure of attention modules: Since I work with imagery I will be talking about using convolutions on feature maps in order to obtain attention maps. If we have a set ...
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How can I weight each point in one-class SVM?

I want to give weights to some data points Specifically, these are points related to anomalies (I'm implementing one-class SVM for anomaly detection) Exactly, I want to consider some data points that ...
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14 views

Is it possible to train a model on files of code and output questions about it?

I want to know if it is feasible to use deep learning to generate homework questions for a course on logic. My input data of programming functions and desired output of respective homework questions ...
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29 views

How to measure the significance of an input feature for the output of a linear layer in a neural network

Suppose I have a simple linear layer $y = xA^T + b$ that is part of a neural network trained on some dataset. The weight matrix $A$ for this layer has the shape ...
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1answer
63 views

Backpropagation after N sequential input-output pass

I'm trying to train a Neural Network in a particular situation -- similar to a genetic algorithm domain as far as I know. I have to run a simulation with a length of $K$ steps. I have a neural network ...
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1answer
20 views

Material(s) for understanding "image channels"

I am pretty confused about the concept of "image channels". I want material that explains the concept of channels from scratch to whatever is required to understand their role in machine ...
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21 views

Is there a term for performance metric like prediction time on a new/unseen example?

The performance entry on Google's machine-learning glossary doesn't mention prediction time on a new/unseen example which is important for production use. Is there a term to refer to that metric?
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32 views

How do I know what a good mean absolute error value is? [closed]

I have just run an MAE calculation for my machine learning models and the results show: SVM MAE = 28.850 deg. Random Forest MAE = 33.832 deg. How do I know what a good MAE value is? What is the ...
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What is the meaning of R2 appearing as a negative in the RandomForestRegressor?

Machine learning model was created by reading an Excel file where data was stored. I applied RandomForestRegressor to create a model that predicts the size of the sieve particles according to pressure,...

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