Questions tagged [machine-learning]
Questions about computer algorithms that automatically discover patterns in data and make good decisions based on them.
1,103
questions
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21 views
time complexity of AA^T
In the context of building of a cocitation weighted adjacency matrix, in terms of A using matrix operations.. what is the time complexity of AA^T?
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0answers
15 views
Discrete action space for MADDPG
I am trying to apply MADDPG, a policy gradient algorithm that uses centralized training and decentralized execution, to a project. In the work of Lowe et al., the actor returns a pmf for a discrete ...
2
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1answer
10 views
Pruning: Outlier Candidate Selection
I am using method 3.3 described in Outlier Detection using Isolation Forest and Local Outlier
Factor .
It states:
Specify a dataset: $๐ท={๐_1, ๐_2, ..., ๐_๐}$.
Here, ๐ is the sample number of ๐ท. ...
0
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1answer
12 views
K Nearest Neighbor using K-D Tree (What ratio makes K-D beneficial)?
I'm trying to implement a KNearestNeighbor model and came across the fact that many professional models use a K-D tree to index the K Nearest Neigbors. I also read that high-dimensional data makes a K-...
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0answers
12 views
How to Plot Logistic Regression using Seaborn? [closed]
I am trying to plot logistic regression model but seaborn regplot does not worked. What should I do?
-1
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0answers
79 views
explain CompuBERT
My question is about the paper Three is Better than One Ensembling Math Information Retrieval Systems (a system used for math information retrieval - both for finding answers and formula search)(code ...
2
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1answer
29 views
Raising to the T in machine learning
What does it mean when in a machine learning paper there is
$(arg)^{T}$, what does the T does to an arg in this 3b1b video on neural networks he puts the: $(w^{l-1})^{T}$
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0answers
80 views
Which features can be considered for neural network based SAT solving?
I'm trying to implement SAT solver, based on backtracking algorithm and BCP. This SAT solver is trying to pick one literal from each clause, from 3-CNF SAT instances. I've implemented a neural network ...
1
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1answer
36 views
PAC learning: success of learning when receiving a sample from a different distribution
I've come across this question and I think I'm on the right track with the idea I just don't really know how to formalize it properly or understand why everything said in the question is required for ...
2
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1answer
14 views
Structuring a Regression Model for NCAA Basketball Spreads/Totals
I have a basic linear regression model that predicts the spread and total points of NCAA college basketball games. My inputs include various efficiency metrics about each team playing each other. A ...
0
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0answers
35 views
Choosing unsupervised learning algorithm for analyzing the spectrum of a linear operator
I am a theoretical physicist, and new to CS.stackexchange, and have a little knowledge of CS, and in Machine Learning (only some general stuff). In physics we often analyze the spectrum of linear ...
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2answers
33 views
What computational complexity classification does autonomous/self-driving cars belong to?
Self-driving car technology continues to attract popular attention and interest in today's media, but how would a computer scientist explain the theoretical nature of the problem? For example, we are ...
3
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2answers
148 views
Proving a certain hypothesis class on a given distribution is not learnable
I had this question in Learning theory, but it's really just a question in probability theory to be honest, so I'm gonna try to rephrase it in a way that really emphasizes what I was trying to do to ...
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0answers
25 views
Coordinate descent for Lasso, Question about algorithm
I'm not sure why the algorithm computes $c_k$ with $\sum_{j \neq k} w_j x_{i, j}$. Why does one need to ignore the $k^{th}$ feature here? I'm not sure how this is derived. Is this the result of taking ...
1
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0answers
15 views
Where can I learn theoretical aspect of Reinforcement learning
Every RL paper has a section with some finite sample analysis/error bound/ convergence proofs. I have difficulty fully understanding such proofs and coming up with my own analysis for my personal ...
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0answers
17 views
What is the Cross Entropy Loss for this output?
Suppose you flip a coin 5 times each with equal probability (let us say 50%, for all 5 coin flips), Would the cross entropy of the function be 1 due to them having no difference in values?
Edit: In a ...
3
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1answer
66 views
AdaBoost - why using such alpha function?
I'm reading the paper where AdaBoost was invented (link), and I couldn't understand why they have chosen the formula ฮฑ_t = 1/2 * ln((1-ฮต_t) / ฮต_t).
snippet:
...
1
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1answer
54 views
Could AI be used to detect when a human is picking survey response options randomly?
Context: I am a clinical psych researcher dabbling in machine learning.
Humans cannot be truly random. Therefore, could machine learning be used to analyze a string of numbers and determine the ...
0
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1answer
32 views
Linear Discriminant Method as Pre-processing for inputs going into a Neural Network?
As the title suggests, I am curious whether Linear Discriminant Method can be performed on a dataset as pre-processing before putting the reduced-dimensional data as input for a neural network for ...
0
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1answer
36 views
PCA applicability
I understand that PCA takes a data set with input size, with output labels, and reduces the inputs to a set of principal components size r, where r < n.
My question is whether or not this can be ...
1
vote
1answer
15 views
How to give the nearest text that the user has written
In my database I have 1500 food dishes, not all are single words, there are compound words like "cheese with dried fruit and nuts". And I have them in 5 languages (de, en, fr, es, it).
My ...
1
vote
1answer
20 views
What is the purpose of manipulating trained layers?
In my course there are a number of exercises which amount to taking a trained residual network and deleting, swapping or repeating layers (without retraining).
Is there any real application for doing ...
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0answers
14 views
When outputting every timestep of an LSTM, is outputs in timesteps with missing training data also affected by overfitting?
I'm referring to things like the "timedistributed" layer in Keras or how in Pytorch LSTM the output is included in every timestep.
An example illustrating my question, say I'm trying to ...
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0answers
27 views
Proving facts in inverse reinforcement learning [closed]
I was going through paper titled "Algorithms for Inverse Reinforcement Learning" by Andrew Ng and Russell.
It states following basics:
MDP $M$ is a tuple $(S,A,\{P_{sa}\},\gamma,R)$, where
...
0
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0answers
10 views
How to use XML format of graphs in Graph neural network?
I have manually labeled directed graphs that I need a GNN to train on and then predict when given a new directed graph, what the labels for the vertexes in the graph should be. My graphs are in the ...
0
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1answer
14 views
What is beta and k parameter in Incremental Decision Tree
I have read this paper https://arxiv.org/pdf/1803.03674v1.pdf for outlier detection problem with real data (online training)
In this paper, the authors used Incremental Decision Tree to build ...
29
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4answers
9k views
Why can't we mimic a dog's ability to smell COVID?
As far as I can tell, we have invented tools and algorithm to:
Detect a wider range of colors at a larger range than humans or any other animals on the planet
Detect sound with wavelengths ...
2
votes
1answer
21 views
Basic exercises on decision trees
I am a pure math person doing some ML self-study and I am pretty lost.
I am trying to solve the following exercises on decision trees:
Exercise 1. Consider the following training set where
$X_1,X_2,...
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0answers
25 views
Why do we use different cost functions in machine learning?
In most machine learning applications, we use Maximum Likelihood Estimation to derive appropriate cost functions. For example, if we assume that our model $f(x;\theta)$ yields the mean of a ...
0
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0answers
30 views
CNN and relearning
I was suggested to post my question here...
An original my question is
https://stackoverflow.com/questions/66421719/cnn-and-relearning
I would like to train my network with CNN. My image data set is ...
0
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0answers
21 views
Does Linear Discriminant Analysis make dimensionality reduction before classification?
I'm trying to understand what LDA exactly does when used as a classifier, i've understood how the dimensionality reduction works and i've understood that the classification task is carried out with ...
5
votes
1answer
160 views
What algorithm do SVMs use to minimize their objective function?
Support Vector Machines turn machine learning linear classification tasks into a linear optimization problems.
$$ \text{minimize } J(\theta,\theta_0) = \frac1n \sum_1^n \text{HingeLoss}(\theta,\...
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27 views
why divide norm of w in svm
So I get this through the math behind the SVM (Support Vector Machine), and I get this formula
$$(w^T)(x_1-x_2) = 2.$$
We then divide both side with norm of $w$ then we get the new formula
$$ \frac{w^...
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0answers
8 views
Which model to apply on such panel data with so may rows but for each unique id rows are 6-8 rows per unique id?
I am new to such panel data where I have multiple observation for same ID in different Quarter and I am not sure what kind of machine learning algorithm I can apply.
I have data from Q1-18 till Q4-...
0
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0answers
7 views
How is the dimensionality of the volume in ConvNets determined for a general case?
In ConvNets, I understand how the dimensionality of a flat image changes after convolving it with a single filter. For example, if you convolve a P x P x 1 image (assume no padding) with a filter with ...
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0answers
24 views
Looking for references for real-world scenarios of data-poisoning attack on labels while doing supervised learning
Consider the following mathematical model of training a neural net : Suppose $f_{w} : \mathbb{R}^n \rightarrow \mathbb{R}$ is a neural net whose weights are $w$. Suppose during the training the ...
0
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1answer
24 views
Is any of the assumptions hold true for the mentioned?
Suppose the use of linear regression. The result of the MSE is 120.5(mean squared error) for the train-set. Wev'e reached the minimum of training the data.
Is it possible that by applying Lasso(L1 ...
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0answers
12 views
What is spatial information in neural network? [duplicate]
The architecture of the CNN model contains several convolutional layers, non-linear activations, batch normalization, and pooling layers. The initial layers learn the low-level concepts such as edges ...
0
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1answer
28 views
Is it possible to do face recognition with just the eyes?
Assuming the input photo is focused on a person's face, if the person is wearing a surgical mask, most face recognition software fail to identify the subject's face.
Most facial landmark models are ...
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1answer
49 views
Kohonen, 1 dimension, SOM, puzzle
We consider training one-dimensional open map of Kohonen with neurons in one-dimensional input space. We assume it is completed the phase of the device and the weights $w_i$, $i = 1,2, \dots, N$, are ...
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0answers
22 views
Even distribution of weighted sums across a period of time
I want to automatically&optimally split and distribute a number in several buckets as evenly as possible across a period of time.
Example:
5000 across 12 months, in 6 buckets, with weights 15%, ...
1
vote
1answer
55 views
How does Gradient Descent treat multiple features?
As far as I know, when you reach the step, in a gradient descent algorithm, to calculate step_size, you calculate ...
1
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0answers
36 views
Reinforcement learning and Graph Neural Networks: Issue with entropy [closed]
I am currently working on an experiment to link reinforcement learning with graph neural networks.
This is my architecture:
Feature Extraction with GCN:
there is a fully meshed topology with ...
0
votes
1answer
13 views
What exactly is label noise?
I've been doing research on precollege summer programs, and one ongoing project that has come up is "Improving Label Noise Robustness with data Augmentation and Semi Supervised Learning". So,...
1
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1answer
23 views
Is transfer learning applied on only similar datasets only?
I am trying to make a CNN model on different brands of logos . Firstly , I wrote a CNN from scratch and trained it on which I got 70% accuracy, I have total 40 classes and each class has 100 images . ...
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0answers
46 views
Best algorithm/model to establish relevance between events utilizing mixed data type (Tags, Time, x_coordinate, y_coordinate)? [closed]
I'm building a relevance ranking system for incidents occurrence and prevention. My goal is to use four attributes to establish relevance: tag (About 500 tags), x_coordinate, y_coordinate and time. ...
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1answer
50 views
What is the Number of epochs with no improvement after which training will be stopped.?
I am trying to make a Convolutional neural network. Training the images of different brands of Logos. Have 100 images per class and there are 40 classes. I have trained the model now want to check ...
0
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1answer
37 views
How does CNN deal with rotation invariant pictures?
I am trying to make a CNN model . Training the image . Want to know that When we apply kernel on image and take out the features of images. That features are rotation invariant or we have to apply ...
0
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0answers
14 views
How do I choose the right model for production?
I am trying to build a classification algorithm having 28 classes. These classes consists of Logo of companies like adidas , Nike etc. I have very low dataset below than 100 images and greater than 70 ...
1
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1answer
32 views
Which pretrained model will be best for my dataset?
I am trying to build a classification algorithm having 28 classes. These classes consists of Logo of companies like adidas , Nike etc. I have very low dataset below than 100 images and greater than 70 ...
