Questions tagged [machine-learning]

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

Filter by
Sorted by
Tagged with
0 votes
0 answers
16 views

Object detection/tracking with deep learning

I am getting familiar with video detection/tracking using neural networks. Specifically, I am referring to algorithms that take in input an image or a sequence of images and, for each frame, it ...
Xvid's user avatar
  • 1
2 votes
0 answers
21 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 ...
Harry's user avatar
  • 21
2 votes
0 answers
28 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 ) ...
Coping Forever's user avatar
0 votes
1 answer
28 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 ...
EngineerP's user avatar
1 vote
0 answers
32 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 ...
Max's user avatar
  • 11
1 vote
0 answers
129 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 ...
desert_ranger's user avatar
0 votes
0 answers
17 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 ...
user185887's user avatar
1 vote
1 answer
18 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 ...
BlockchainThomas's user avatar
0 votes
1 answer
30 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 ...
b1ackf0x's user avatar
0 votes
0 answers
15 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 ...
Rafał Sokalski's user avatar
3 votes
1 answer
156 views

How did the authors of Spice 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 ...
desert_ranger's user avatar
0 votes
1 answer
53 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 ...
Everson's user avatar
10 votes
3 answers
889 views

Resources for studying the mathematical foundations of machine learning, for someone from a math/physics background

I am a soon-to-be physics graduate student with a background in theoretical and experimental cosmology. In my work, I've often found myself applying machine learning models and techniques for the ...
10GeV's user avatar
  • 201
1 vote
1 answer
111 views

Learning eigenvalue decomposition

How would you build a fully connected neural network that learns eigenvalue decomposition efficiently? I wanted to build NNs that can predict certain properties about matrices which are NP-hard to ...
Arjo's user avatar
  • 113
0 votes
0 answers
22 views

Reading and answering a text question from a image?

I wanted to see what models would be best suited for providing a image (see the link below) and having the model perform a image to text (OCR of some kind) and laying out the contents of the image in ...
Pie's user avatar
  • 101
-2 votes
1 answer
45 views

Objective function

I want to learn how to write objective functions for any problem or real-life action(not mathematical problems) can anyone suggest a good source? On online I am mostly finding objective functions for ...
Star Lord's user avatar
1 vote
0 answers
35 views

How exactly does minimizing the L2 norm of w in SVR affect the model's ability to fit the data?

I have a solid grasp on why we minimize the L2 norm of the weight vector ( w ) in Support Vector Machines (SVM) for classification problems — it maximizes the margin between classes and helps the ...
dedman2000's user avatar
1 vote
0 answers
24 views

Upper bound via standard manipulation in proof of semi-private learning

I have been reading a paper on private learning [1]. In the proof of lemma 3.3. they claim that $$ 2\left(\frac{2e n_\text{pub}}{d}\right)^{2d}e^{-\alpha n_\text{pub}/4} $$ is upper bounded by $\beta$ ...
TheCollegeStudent's user avatar
0 votes
0 answers
8 views

Q-learning table efficiency

So I am trying to create a Q-learning algorithm to solve simple games, starting with tic-tac-toe. I would like to do so without completely eating away my memory, but all the examples online create a 2 ...
Clement Genninasca's user avatar
0 votes
1 answer
28 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 ...
Tran Khanh's user avatar
0 votes
0 answers
38 views

Incompresensible about $w$ $x$+ $b$ = 0

I don't actually understand the meaning of $w$$x$ + $b$ = 0 when it is defined in support vector machine. In my own understanding, in order for the equation to be true, the hyperplane would always ...
ABC's user avatar
  • 11
1 vote
1 answer
58 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) ---...
Grigori's user avatar
  • 105
0 votes
0 answers
31 views

Finding an algorithm EF[1,1] and PO division for more than two agents

From this research paper I want to write an algorithm for finding envy-freeness(EF) and Pareto optimality(PO) division for more than two agents. We consider the problem of fairly and efficiently ...
A. H.'s user avatar
  • 498
0 votes
0 answers
17 views

Transductive Information Maximization vs classification with feature embedding in higher dimensional spaces?

Recent research work has shown that transductive learning/inference outperforms standard methods that were used before, where people embed features in a high dimensional space and then use the ...
Sandra's user avatar
  • 63
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 ...
SecondOrderConfusion's user avatar
0 votes
0 answers
73 views

How to distinguish between Unrepresentative Train Dataset and minimal overfitting from a learning c

Is this a minimal overfitting or like unpresensitive train dataset? However, I read it on the net but still have a problem with the graph! And then I read the following text on the net and again I ...
Amirreza Hashemi's user avatar
0 votes
0 answers
27 views

Dilated LSTM vs LSTM

I'm currently reading a paper for a literature review related to NLP and its research employs a dilated LSTM model. What is the difference between this and a regular LSTM, or more specifically what ...
paul lacher's user avatar
0 votes
1 answer
33 views

Question about data to train an machine learning IA

I'm trying to create a machine learning program to predict the winner of UFC fights. I want to train my AI with data from previous matches, but I have encountered a problem. I wonder if the algorithm ...
BlizZ's user avatar
  • 1
1 vote
0 answers
59 views

DeepMind Alphadev: How did it use Reinforcement Learning to reduce the search space?

Google DeepMind recently published a new paper which describes how they used a reinforcement learning to discover faster sorting algorithms. A summary of the paper is here and the paper is here. It ...
equis's user avatar
  • 11
0 votes
1 answer
101 views

What is currently the best theoretical book (or notes) about Convolutional Neural Networks?

I have a degree in maths and i am interested in learning about Convolutional Neural Networks from a mathematical/ theory point of view. Can someone point me towards some resource. I already have a ...
PhysicsQuestion's user avatar
0 votes
0 answers
48 views

Solving constrained optimization problem using PyTorch: Minimizing L1 norm of $\vec{x}$ subject to $\vec{x} = \mathbb{A^{-1}}\vec{y}$

My goal is to solve the above-constrained minimization problem where my vector to be optimized is x. The matrix A and the vector ...
Dixshant s's user avatar
1 vote
1 answer
230 views

Why GPT model is a higher order hidden markov model

I have read the GPT-1 paper, and my understanding is that it works as follows: $U$ is input tokens, $h_0=UW_e+W_p$, $h_i=\text{transformer_block}(h_{i-1})$ and the output is a probability vector $P(u)=...
123's user avatar
  • 11
2 votes
1 answer
60 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 ...
Koenig Lear's user avatar
1 vote
0 answers
106 views

Transductive Learning vs Inductive Learning in Machine Learning

Several recent research work has shown that transductive learning/inference outperforms inductive learning/inference during classification problems. This has been found in few-shot learning, other ...
Sandra's user avatar
  • 63
-1 votes
1 answer
74 views

How to calculate the growth function of a hypothesis class?

I have this hypothesis class: H = {ha : R → {0, 1} | a > 0, a ∈ R, where ha(x) = 1−a,a = { 1, x ∈ [−a, a] 0, x ̸ ∈ [−a, a] } I need to compute the growth function for m>= 0. So I think that this ...
anonymus's user avatar
1 vote
1 answer
41 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 ...
zhan danning's user avatar
0 votes
2 answers
70 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 ...
Ashrjz's user avatar
  • 1
0 votes
0 answers
82 views

Create a simple Neural Network of n layers in python from scratch with numpy to solve XOR example problem using Batch Gradient Descent

I'm a young programmer that was interested by machine learning. I watched videos and read articles about the theory behind simple neural networks. However, I can't manage to set it up correctly. I've ...
NolanGio's user avatar
-1 votes
1 answer
57 views

How to show that an algorithm is not fair?

Algorithm fairness is the field of research aimed at understanding and correcting biases like these. It is at the intersection of machine learning and ethics. Specifically, the field includes: ...
Shi's user avatar
  • 35
1 vote
2 answers
39 views

Analyzing network architecture with TDA

I'm doing Neural Architecture Search (NAS) by varying the number of layers and neurons per layer for a neural network (connections are feed-forward throughout), and then training it on a fixed task to ...
user314159's user avatar
1 vote
0 answers
24 views

PAC Learning LTFs with random classification noise and membership query

How would one design an algorithm to PAC learn linear threshold functions with random classification noise using membership queries? More formally, the learner has access to unlabeled examples from an ...
envyul's user avatar
  • 11
1 vote
1 answer
45 views

How can a Machine Learning model predict this classification problem?

I am pretty new to ML and have a basic but fundamental question. Let’s imagine we want to create a simple Sentiment Analysis model using Machine Learning not Deep Learning algorithms, so we need to ...
Z Bokaee's user avatar
1 vote
0 answers
71 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 ...
ryan chandra's user avatar
0 votes
1 answer
52 views

Difference between paradigm and Algorithm in Machine Learning

Give a class $\mathcal{H}$ of predictors from the set X to $\{0,1\}$. Here each $h \in \mathcal{H}$ is a function from $h: X \longrightarrow \{0,1\}$. The Empirical Risk Minimization is defined as $$...
Shi's user avatar
  • 35
0 votes
1 answer
69 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 ...
Dragoș Constantin's user avatar
0 votes
0 answers
21 views

In the next ten years, what modifications to hardware architecture do you anticipate being made to enhance machine learning performance?

How do you believe prominent deep learning ASIC makers such as Nvidia and Google will alter their hardware design over the next decade to meet the exponentially expanding need for extra computing to ...
matty 's user avatar
0 votes
0 answers
9 views

Role of State Dynamics Target Network in DDPG

I am trying to create a variant of DDPG in MATLAB that has no action-value $\langle Q \rangle$ net, but that instead works with networks $\langle V \rangle, \langle f \rangle, \langle r \rangle$, and ...
Vera Leighton 's user avatar
0 votes
0 answers
16 views

Bellmann Error with Time Step

I define the return $G$ as the discrete time integral of the reward $G = \Delta t \sum_{t = 0, ..., T}r_t$. In supposing $\Delta t = 1$ throughout, my instructor wrote the following formula for the ...
Vera Leighton 's user avatar
0 votes
0 answers
23 views

Facial Recognition

I need an algorithm that can do facial recognition in a video based on a training set. For any faces detected, it should give an output as how much the face matches with the faces in the training set. ...
Arinjoy Pramanik's user avatar
0 votes
0 answers
27 views

If the data that I input for locality sensitive hashing is sensitive, should its resulting hash also be considered sensitive?

Is it correct to assume that if the input that I hash is sensitive, that I should also consider the locality-sensitive hash generated for that input to be sensitive? I presume that, if an attacker has ...
Pierre Duluth's user avatar

1
2 3 4 5
25