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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Unclear points in scaled Euclidean distance
The following is from a machine learning book.
I did not understand the explanation given in the figure caption. Could some expert make it clear? Why is the stretching class-dependent for the center ...
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Why does PCA work well while the total variance retained is small?
I'm learning machine learning by looking through other people's kernel on Kaggle, specifically this Mushroom Classification kernel.
The author first applied PCA to the transformed indicator matrix. He ...
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Likelihood function for Gaussian Discriminant Analsis
Im trying to understand how the likelhood function for gaussian discriminant analysis is derived. I self studying Murphy's Probabilistic Machine learning, and in it, he states the likelihood function ...
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Will parameter sweeping on one split of data followed by cross validation discover the right hyperparameters?
Let's call our dataset splits train/test/evaluate. We're in a situation where we require months of data. So we prefer to use the evaluation dataset as infrequently as possible to avoid polluting our ...
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Model suggestion for AI based scaling
We are exploring the idea of scaling elements within a UI container based on the given size.
The container is represented by a json object, for example:
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Using the definition of APAC learning and uniform convergence in practice
I am currently studying "Understanding Machine Learning from Theory to Practice" written by Shai Shalev-Shwartz and Shai Ben-David. I want to understand how i can use the Definitions and ...
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How can we construct a skewed noise distribution using the maximum likelihood approach?
When the probability of observing a large positive error is larger than the probability
of observing a large negative error in binary classification, how can this be modelled by a skewed noise ...
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why we use learnable positional encoding instead of Sinusoidal positional encoding
In the original paper of transformers they using positional encoding to capture the position of each word in the sentence and for calculate that it using sin and cos ,like shom in the image. In Bert ...
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How to Implement a Text-Based Question Answering System for PDF Documents using Machine Learning?
As a beginner in machine learning, I've completed a basic text classification project in university. Now, I'm eager to build a system that can answer specific questions from a large collection of PDF ...
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How does using complex weights in a neural network affect performance?
If you switch a neural network from real weights to complex weights, you're roughly doubling the size of the network, and increasing the computational load by a factor of 2 to 4. My question is, in ...
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1D CNN with Single vs. Two Channels for Number Image Recognition
I am taking images of numbers as input, in a convolutional neural network and building a model to predict the number.
In particular, I am building a one dimensional convolutional neural network with ...
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Which type of ML algorithm takes the least amount of time for training?
I am doing research on proteins.
I have 17,000 *.CSV files on my hard disk. These files represent the chains of proteins. I want to use these ...
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Why same learning rate for slope and intercept not working in Linear regression?
I'm a new student in AI, currently learning linear regression. I used the california housing dataset for doing my experiments. My goal is to predict the 'population' column based on the 'total_rooms' ...
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High-resolution 3D human digitization from a single image or multiple images
Reference
A 2020 tool converts a single image or multiple images of a full body to a 3D OBJ file. Internally, it creates the SDF - signed distance field - and then uses the marching cubes to generate ...
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Is $(y_i - \hat y_i)x_i$, part of the formula for updating weights for perceptron, the gradient of some kind of loss function?
A post gives a formula for perceptron to update weights
I understand almost all the parts of it, except for the part $(y_i - \hat y_i)x_i$ where does it come from? Is it the gradient of some kind of ...
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What is the concept of pruning a tree in Machine Learning regression problems?
What is the concept of pruning a tree in Machine Learning regression problems? I am confused and a simple explanation would be great.
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Scrabble game using machine learning
I've been thinking if machine learning can be used to play the game Scrabble. My knowledge is limited in the ML field, thus I've seeking some pointers :)
I want to know how could I possibly build a ...
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How do you interpret this train vs test accuracy scores? is the model under or over fitting?
What does this difference in train and test accuracy mean?
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How the Q,K,V be calculated in multi-head attention
I want to understand the transformer architecture, so I start with self attention and I understand their mechanism, but when I pass to the multi-head attention I find some difficulties like how ...
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Literature suggestions for transformers
What are the best educational sources for learning about transformers, what is the go to literature for a mathematician who considers themself a beginner in the subject?
Books, lecture notes, research ...
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Drum sound classification using RNN issues - help needed
I am new to the field of machine learning, even tho I have solid background in semi-related fields (am control system engineer by trade) and as a hobby project I wanted to work a bit with sound ...
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Integration of Sentiment analysis in CRM
What is the process for integrating sentiment analysis in a CRM? What I am searching for is a system which analyzes the customer comments or reviews using the CRM and finds out the customer sentiment ...
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Do neural networks have a perception of space, regardless of dimensionality?
Suppose I have a model M which outputs a three-dimensional tensor of size 3x3x3.
I have another model N which outputs a one-dimensional tensor of size 27.
Train both models on some arbitrary objective ...
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Could LLMs perform the autoregressive generation with probability vectors instead of choosing a discrete token every time?
As I understand it, GPT-style LLMs take a sequence of tokens as input and output a token probability vector. The first thing that happens to an input token is that it goes through
the input embedding, ...
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Machine Learning Algorithm for identifying the factors contributing to academic performance of students
I have a dataset with several qualitative and quantitative attributes, including age, location (longitude, latitude), city, parent occupation, family size, GPA etc. My task is to find the attributes/...
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NEAT can't solve XOR completely
I'm currently implementing the NEAT algorithm. But problems occur when testing it with problems which don't have a linear solution(for example xor). My xor only produces 3 correct outputs once at a ...
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Since ReLU activations also result in a sparse network, does it have the same "feature selection" property as L1 regularization?
From Deep Learning (Courville, Goodfellow, Bengio), a ReLU activation often "dies" because
One drawback to rectified linear units is that they cannot learn via gradient based methods on ...
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What does "All store and access operations (for S(t) , A(t), and R(t)) can take their index mod n + 1" mean?
It's from the book Introduction to Reinforcement Learning. Second edition, chapter7: n-step Bootstrapping, page 147, n-step Sarsa.
I made the algo work, but I still don't understand the phrase.
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What kind of algorithm or approach can I use to find a specific type of object in an image?
What kind of algorithm or approach can I use to find a specific type of object in an image?
In particular, I am interested in finding an object like a windmill in an image taken, for example, from ...
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Why do I get small probabilities when implementing a multinomial naive Bayes text classification model?
When applying multinomial Naive Bayes text classification, I get very small probabilities (around $10e^{-48}$), so there's no way for me to know which classes are valid predictions and which ones are ...
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Is it easier to use back-propagation or genetic algorithms to teach an artificial intelligence?
I am making a very simple neural network for a school project, and I would like to know what the best and easiest way to "teach" a neural network would be. From what I know, backpropagation ...
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Distribution Based Mapping of Augmented (Noisy) Image and Clean image?
I have a task of training a Denoising Autoencoder which will be augmented MNIST dataset and and I have to reproduce the clean image, I have to use the ResBlocks in the Encoder(Conv Layer) as well as ...
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Are PreLU and Leaky ReLU better than ReLU in the case of noisy labels?
Let's assume I want to build a semantic segmentation algorithm, based on Multires-UNET. My GT-masks are messy and generated by a GAN, but they are getting better and better over time. The goal is ...
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Is there a way to update the neural network to fit the new data without the time required for retraining?
I built a basic neural network in MATLAB. The neural network classifies points on the X-Y axis system into two classes (0 and 1).
(I try to get the function that represents a shape from this photo)
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How the Critic is used to train the Actor in Actor-Critic network
I understand the general idea behind the Actor-Critic architecture. The actor maps state to action, and the critic maps state + action to reward.
But I don't fully understand how the critic output (...
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How to predict time signal based on multi-input signals?
I would like to approximate the following relation by a neural network
$y = \mathcal{f}(x_1(t),x_2(t))$
Here, I have only one output variable that is a function of 2 other variables which vary in time....
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Confusion about the proof that optimizing InfoNCE equals to maximizing mutual information
In the appendix of Representation Learning with Contrastive Predictive Coding, van den Oord et al. prove that optimizing InfoNCE is equivalent to maximize the mutual information between input image $...
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Adding Feature in HGNN to Count Connections to Types of Nodes
So I'm making a HGNN currently in which the number of connections a node has to other nodes of a certain type matters. Its a social network, so I care about how many person-person connections a person ...
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How do I poison an SVM with manifold regularization?
I'm working on Adversarial Machine Learning, and have read multiple papers on this topic, some of them are mentioned as follows:
Poisoning Attacks on SVMs: https://arxiv.org/pdf/1206.6389.pdf
...
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Gradually increasing CPU load on using sentence embeddings model with kmeans
I am having a ML based production application, using flask, deployed on GCP server using gunicorn workers. In each incoming request, a text sentence is received.
It is using sentence transformers (All-...
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What might be the suitable machine learning algorithm to train a model suitable for forecasting a patient's urine output?
The task involves developing a machine learning model trained on urine output trends, clinical parameters, medications, and fluid input of patients to predict their future urine output.
What machine ...
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Embedding Quality of Transfer Learning model vs Contrastive learning model
I am working on Contrastive learning which is a technique to learn features based on the concept of learning from comparing two or more instances.
The downstream task is a classification problem.
...
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Can/should a reward function depend on something other than state in a DQN
Question: Is it OK to have a reward function on a DQN or any RL algorithm that depends on variables other than the enviroment state? I'm asking because, so far I'm learning from tutorials, but I've ...
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How do transformer-based architectures generate contextual embeddings?
How do transformer-based architectures like Roberta generate contextual embeddings? The articles I've read keep saying that transformer encoders work bidirectionally. Because of self-attention, they ...
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What consequence would a polynomial time algorithm for SAT have on AGI?
$P$ vs $NP$ is a famous problem. We generally believe $P\neq NP$. However suppose there is a polynomial time algorithm of order say $O((n+m)^2)$ or $O((n+m)^3)$ (a low degree polynomial complexity ...
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ML Model for Route Optimization in Manufacturing
I am looking for a ML Model for route optimization within a factory. I am starting simple with optimizing 1 aisle (2 rows). We have a bunch of criteria, and it would be dependent on the data at that ...
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Why the cost/loss starts to increase for some iterations during the training phase?
I am trying to build a recurrent neural network from scratch. It's a very simple model. I am trying to train it to predict two words (dogs and gods). While training, the value of cost function starts ...
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Is it necessary that the number of samples of one class be balanced with other classes in a classification problem?
Consider a classification problem using machine learning techniques (e.g. malware detection). In such a problem, is it necessary that the number of samples from each class (in the mentioned example, ...
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How exactly does MICE imputation combine multiple datasets into one?
I'm trying to understand Multiple Imputation with Chained Equation (MICE) imputation process (a statistical method for imputing missing data). I have read some articles and I have understood how the ...
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Does it classify as Machine Learning?
I have a gaussian distributed time series ($X_t$) with some parameters in my experiment. Suppose I want to know the mean $\mu$. If I define another time series $Y_t$ such that $Y_t=X_t-a$ for all $t$. ...
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