Questions tagged [machine-learning]

Questions about computer algorithms that automatically discover patterns in data and make good decisions based on them.

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12 views

Binary Classification- Non-Differentiable Loss Function

For binary classification using linear regression, we pass the output z of the linear regression through the sigmoid function so that if the linear regression takes an input x which should be ...
1 vote
1 answer
84 views

Initialization of embedding space value for VQ-VAE

I am trying to study a research paper about VQ-VAE: Neural Discrete Representation Learning, Aaron van den Oord, Oriol Vinyals, Koray Kavukcuoglu, NeurIPS 2017. I have difficulty to understand the ...
0 votes
1 answer
102 views

A confidence interval algorithm for Disagreement coefficient

My question has to do with the disagreement coefficient in active learning. I've been trying to solve the following question, where I need an algorithm to derive a confidence interval for $\theta$, ...
3 votes
1 answer
173 views

How did this work apply weakest precondition rule on their example car problem?

While reading the example given in [1]., I couldn't understand how the authors set up the logic to compute the weakest preconditions (wp) in their car example in section 4.2. The dynamics of the ...
0 votes
1 answer
93 views

SMO, Random forest and Bayes net algorithms: why does Random forest perform better?

I analyzed a dataset using those 3 different algorithms. As I can see, Random forest performs better in most cases. My dataset is composed of 4000 instances of two classes (class A 2000 elements, ...
0 votes
1 answer
76 views

Classification accuracy based on top 3 most likely classifications

My goal is to recommend jobs to job seekers based on their skill set. Currently I'm using an SVM for this, which is outputting one prediction, e.g. "software engineer at Microsoft". However, consider ...
1 vote
0 answers
58 views

How to convert wp equations into linear algebra equations?

I am reading this paper [1], wherein the authors first formulated a safety constraint in terms of wp equations. Then, they converted the equations into linear algebra form. As per section 4.3, their ...
2 votes
2 answers
74 views

How many games will a “MENACE” tic tac toe computer take to train

I recently read about the “computer” built out of matchboxes designed by Donald Michie that could teach itself how to play tic tac toe. Here is the Wikipedia article about it: https://en.m.wikipedia....
1 vote
1 answer
28 views

Why we need at most $2n$ examples to determine an axis aligned rectangle

In Ben-David & et al.'s Understanding Machine Learning, the authors wrote: Let $\mathcal{H}_n$ be the class of axis aligned rectangles in $\mathbb{R}^n$ , namely, $$ \mathcal{H}_n = \{h(a_1,\dots,...
0 votes
1 answer
65 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 ...
0 votes
0 answers
46 views

Sensibility analysis for a discrete output data

I have a dataset with 1055 samples and 168 features. I want to remove some features in order to train a model on my dataset. The model should output a binary field (either 0 or 1 that is known). Some ...
0 votes
2 answers
606 views

What are the drawbacks of Normalized Mutual Information clustering evaluation method?

What are the drawbacks of Normalized Mutual Information (NMI) clustering evaluation method? For evaluating what clustering algorithms, is the NMI evaluation method suitable?
6 votes
1 answer
602 views

Machine learning approach to auction game

I am newbie with machine learning. In order to learn more I decided to try solving a specific problem/game that I have in mind. The problem is the following: I have a list of $N$ items which are ...
1 vote
1 answer
66 views

how Is Convergence of machine learning bounded O(n)?

All charts of machine learning performance (y-axis:accuracy 0.0-1.0) across epochs have the below shape. Is there any research that explain how this convergence is bounded O(n) where n is the number ...
5 votes
1 answer
402 views

reinforcement learning in gridworld with subgoals

Andrew Ng, Daishi Harada, Stuart Russell published a conference paper entitled Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping. There is a specific example ...
0 votes
1 answer
346 views

What is the difference in SMO algorithm for SVM and SMO for one class?

Please let know if this is not the correct forum to ask this question. If not can anyone please tell where can I ask this question? I am trying to understand the difference between the paper : https:/...
0 votes
1 answer
24 views

Advice for better performance neural network

As a beginner in neural networks, I'm currently working on a project for predicting winning fighters in MMA matches. I understand that this is a complex task due to the nature of the sport. Currently, ...
4 votes
0 answers
120 views

Constructing worst case for L* by D.Angluin

I'm working on constructing deterministic finite automata (DFAs) with a specific learning complexity when using the L* algorithm developed by Dana Angluin. My goal is to create a DFA of size ( n ) ...
0 votes
0 answers
7 views

Partial derivatives wrt input and weights in CNNs

I'm working on understanding the backpropagation process in convolutional neural networks (CNNs) and am having some trouble comprehending how to calculate the partial derivatives of the output of a ...
0 votes
2 answers
84 views

ML model to pick to classify data with inputs as an array of strings?

I have a medical dataset, which is something like this ...
1 vote
1 answer
48 views

Machine Learning algorithm to learn submodular set functions

I'm looking for some machine learning algorithm to train on data that are sampled from some submodular set function and I want the learned model predictions also obey submodularity. For example linear ...
1 vote
1 answer
50 views

meaning behind activation function

I have been recently thinking about activation functions and the explainability. For sigmoid and tanh activation functions, I am thinking of them to be similar to logistic regression as the output of ...
2 votes
1 answer
100 views

Why is the input of the algorithm in "Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation" a matrix?

My question is about the algorithm in the article Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation, as described in the picture below. It is written at beginning ...
0 votes
0 answers
19 views

Textbooks to study machine learning as a TCS/FM/symbolic AI researcher

I work in the fields of theoretical CS, formal verification, and symbolic AI. Here's my DBLP link to have a picture of my background: DBLP Can you suggest me resources (good graduate-level textbooks) ...
0 votes
0 answers
110 views

Is deepfake detection viable?

I'm thinking of doing a project on deepfake detection, but I'm not entirely sure if it is viable. Based on my understanding, how it works is that deepfake generation programs have a generative and ...
2 votes
0 answers
25 views

Representational power of Neural Neural Networks without a bias term

In a fully connected Neural Network, each perceptron has it's bias term $b$ which is learnt. Often (example, in Linear/ Logistic Regression), when we don't want to treat this bias term explicitly, we ...
0 votes
1 answer
56 views

Main subjects to learn Artificial Intelligence in CS

In my PhD, I will work with ML models. However, I will only use ready-made models as a tool, but I want to delve deeper into Artificial Intelligence not just to use ready-made models, but to ...
1 vote
1 answer
110 views

How to do supervised learning without knowing the target

I'm working on an optimization algorithm that I think could be considered machine learning, but I'm not sure. Basically I have a model that I want to optimize by adjusting its parameters. I don't ...
14 votes
1 answer
2k views

Alternatives to SVD for rank factorization

I have rank-deficient matrix $M \in \mathbb{R}^{n\times m}$ with $\text{rank}(M) = k$ and I want to find a rank factorization $M = PQ$ with $P \in \mathbb{R}^{n \times k}$ and $Q \in \mathbb{R}^{k \...
0 votes
1 answer
34 views

Python Scikit-Learn transformation

I am trying to learn Scikit_Learn and build an ML model. I am learning from "Hands-On Machine Learning with SciKit-Learn, Keras & TensorFlow". In Chapter 2, there is a review of an ...
1 vote
0 answers
33 views

How does a One-Class SVM work?

My teacher explained the function of a one-class SVM to us. However, the sketch used is not correct from my point of view. I understand the function of a one-class SVM as follows: The SVM is trained ...
2 votes
1 answer
303 views

How many pixels support each neuron in multi-layer CNN?

I'm studying for a computer vision module and I'm on the deep learning topic, in one past paper we have the following question: Given that a convolutional neural network has five convolution layers (...
1 vote
1 answer
95 views

Method for combining derivative free optimization results of different data inputs

I am working on an algorithm that has multiple fixed parameters. The algorithm analyzes time series data and spits out a number. The fixed parameters need to be such that this number is as small as ...
1 vote
0 answers
144 views

How to deal with missing variables when utilizing weakest precondition for verification?

I am reading the example given in [1], section 4.2. It deals with applying weakest precondition (wp) rules to ensure that the velocity of a car doesn't exceed a certain limit. We have the following ...
0 votes
1 answer
303 views

Pac learnable when changing distribution

if H is Pac learnable according to Definition 3.1 (PAC Learnability) A hypothesis class H is PAC learnable if there exist a function $m_H\colon(0,1)^2 \mapsto N$ and a learning algorithm with the ...
0 votes
0 answers
20 views

Explanation for the expression of positional encoding in NeRFs

I was reading the NeRF paper recently (https://arxiv.org/pdf/2003.08934.pdf), and under the positional encoding section, I see that the authors propose usage of the following function for transforming ...
1 vote
1 answer
23 views

Is a Convolutional Neural Net a special case of DNN? If so, how can the convolutional layer be modelled?

In literature, Convolutional Neural Nets (CNNs) are presented as a special case of Deep Neural Nets (DNNs) (e.g., here). I do not understand how the convolutional layer can be implemented through a ...
1 vote
1 answer
65 views

Impact of label flipping on ROC-AUC score

I was exploring a problem of label flipping in the target column: Problem: I have a data frame with just two columns: Predicted probability | Target. Target column is binary (contains only 0 and 1) ---...
0 votes
1 answer
31 views

Strange notation in the article about sparse self-attention

While reading an article devoted to the sparse self-attention, I came across a notation that was not very clear: $$ Attend(X, S) = \Big( a(x_i, S_i) \Big)_{i∈{\{1,...,n}\}} $$ What means $\Big( \space ...
0 votes
1 answer
88 views

Confused on Variational AutoEncoder

I'm a bit confused on how Variational Autoencoders are trained. In particular I'm confused on how the latent variable is generated for each input. My questions are as follows: When running ...
1 vote
1 answer
46 views

Resources to learn NLP

I am an undergraduate student in mathematics. I have a fair bit of experience with deep learning in computer vision research and am willing to dabble into NLP. I hope that things won't be very ...
2 votes
1 answer
450 views

How do I prove that the Perceptron bound for mistakes is tight?

How do I prove that the Perceptron bound for mistakes is tight? I need to prove that for any amount of given data points, the total amount of updates (mistakes) that the algorithm will make is $\frac{...
0 votes
0 answers
16 views

Best solution architecture for: Intelligent chat to retrieve information from specific sources

I am trying to design best solution for a chat application based on LLM model which has set of functions and based on user input can retrieve the information. I would like to ask you for some ...
0 votes
1 answer
97 views

Signal translation with Seq2Seq model

I'm currently doing some research on signal processing and I got a dataset which includes the signal in itself and its "translation". So I want to use a Many-to-Many RNN to translate the ...
0 votes
1 answer
80 views

Mapping categorical data in K-nearest neighbour

I have a data set which contains categorical data, for example: ...
0 votes
1 answer
23 views

Latent variable model from measure theory perspective

It's common in machine learning papers to see things like $p(x,z|\theta)$ or $p(x|z)$. Where $x$ is usually the data vector, $z$ the latent vector and $\theta$ the model parameter, like network ...
0 votes
1 answer
43 views

Conditional probability in Expectation Maximization (EM)

I've got the following equation: $p(j = 1 | x, \theta) = \frac{p(j=1,x | \theta)}{p(x | \theta)}$ Why does it hold? Or maybe, how do I use Bayes Theorem in this case, i.e. if we do not only have $p(j =...
0 votes
1 answer
71 views

Question about the proof for the sample complexity of axis-aligned rectangles

The classical proof for the sample complexity of the hypothesis class of axis-aligned rectangles usually begins by stating that our $A(S) \subset R^*$, where $R^*$ is the target function. My only ...
0 votes
1 answer
30 views

PAC-learning framework: Why the sample size must also be polynomial if the full sample is received by the algorithm

On page 11 in the book Foundation of Machine Learning, 2nd edition MIT press, the authors wrote: Definition 2.3 (PAC-learning) A concept class $C$ is said to be PAC-learnable if there exists an ...
1 vote
1 answer
75 views

Interactions between predictive analytics, ML, and case-based and rules-based reasoning

For my studies on economy, I work on prediction of judicial decisions. I don't really understand the interactions between several concepts: predictive analytics machine learning case-based reasoning ...

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