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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Which epoch is the best for me to choose?
I have trained my deep learning model. I also saved the validation loss to a file and plotted on a graph
I have $2$ questions for this:
Does the validation loss look normal? Is there any issue with ...
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What is a "multinomial model" in machine learning?
What is the mathematical definition of "multinomial model" in machine learning?
I will be happy for a good definition plus an example.
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How to decide on how to define the output function for a neural network?
What I want my simple ML model to do is for a certain case file, to take the type of crime and the amount of damage(converted into dollars), and to make a judgment (in dollars incorporating price for ...
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Prefix tuning in LLM uses learnable vectors to fine tune the model
I would like to implement a new architecture for Transformer.
Below description is my thought.
Prefix tuning in LLM uses learnable vectors to fine tune the model.
Is there a way to use the output ...
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Could an analysis of GPT4's WAIS score be published?
Similar to this: https://www.scientificamerican.com/article/i-gave-chatgpt-an-iq-test-heres-what-i-discovered/
But more detailed and in depth (subtest breakdown, including image analysis, etc.), WAIS-...
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Do GANs have constant running time?
After the model is trained, you just need to input random noise and the generator will output an image, does this mean GANs have constant running time ? I'm asking about both naïve GAN and variants of ...
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Is it possible that Precision and Recall increase together?
Usually, it is said in ML that there is a trade-off between Precision and Recall.
I wonder if it is possible that Precision and Recall can increase together?
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What are the advantages of GANs over Diffusion Models in image generation?
Diffusion Models have recently gained popularity in the field of image generation, with widely used products such as Stable Diffusion employing this approach and yielding impressive results. While ...
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is there a mathematical explanation of precision and recall tradeoff?
Is there a mathematical explanation of precision and recall tradeoff? Ie, is it possible
mathematically to see that as you increase one, the other decreases?
$\text{Precision}=\frac{TP}{P^*}=\frac{TP}{...
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Question about the Conditioning Augmentation technique?
In the paper StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks, the goal is to convert text descriptions into images. The text encoder encodes the ...
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What kind of ML/AI approach might work well for "edge" detection in a function?
I have a function that looks something like this (in blue):
I'm curious to hear what kind of ML/AI algorithm might be useful to detect the approximate x0 value at which the function y(x) ...
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How to detect negative (absence of) an object?
I want to detect the people that are NOT wearing PPE vests using a pre-trained object detection model like YOLO or Grounding Dino.
The models are able to detect people and vests separately, but I am ...
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What is a pipeline in machine learning?
I have heard the term "pipeline" used in many different contexts. Now I'm trying to bring some clarity to the terminology: What exactly is a "pipeline" in machine learning?
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how to determine the number of units for dense layer for transfer learning?
I'm using MobileNetV2 for classification, and I want to add dense layers(i remove the last layer of the MobileNetV2 model). How do I choose the number of units for the dense layer after obtaining the ...
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In k-NN, how does the condition $k(N)/N \to 0$ ensure that all the k nearest neighbors are close to a given test point $\mathbf{x}$?
Consider the k-NN algorithm and let $k(N)$ be the choice of k as a function of N (data points).
For $N \to \infty$, if $k(N) \to \infty$ and $k(N)/N \to 0$, then k-NN converges to optimal classifier.
...
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Can't get a correct accuracy on tabular data using deep learning
This is my first message here, and I would like to seek some assistance !
I have a technical test for a job that I really want, and I have 10 days to complete it. I've attempted to work on it, but I'm ...
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Meaning of "error on the test point x" in optimal classifier for binary classification
Let f(x) be optimal classifier for binary classification where output is modelled noisy.
What does it mean "f(x) makes a mistake only if there is an error on the test point x"? Basically,
...
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Is it possible to do machine learning on encrypted images?
I wuold like to know if it is possible to train a CNN to classify images (like nsfw detection) without receiving any intelligible information about it ?
By applying some kind of one-way mask before ...
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The training process of a conditional GAN
For example, consider a dataset like MNIST. I give the conditional vector to produce only the number $7$ for both the generator and discriminator. In the following scenarios, what will the ...
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Can mini-batches for stochastic gradient be balanced but not representative of the training data?
When we construct mini-batches for stochastic gradient, it is important to ensure that the different mini-batches are balanced (for example, in case of classification they contain the same ratio for ...
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Neural network learns to mimic distribution of classes in dataset instead of using signal from input
I'm trying to implement example from a classic AI paper named "Learning representations by back-propagating errors" by Hinton et al.
Example aims at training network able to predict third ...
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NLP "small" model to improve "big" model
When training the model for NLP is it important to get rid of data which has "bad semantic" for learning process?
My plan is to create a "small model" which can decide whether data ...
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the best choice to reduce a features vector
i have 1200 features highly correlated , and i want to reduce those number of features
so the best choice is use feature selection or dimensionality reduction? and which method is the best in this ...
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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 is laplace distribution less sensitive to outliers than normal distribution?
Why is laplace distribution less sensitive to outliers than normal distribution?. The following is the content.
(See page 103 of http://smlbook.org/book/sml-book-draft-latest.pdf)
"Also, in the ...
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What is the potential issue of nested neural networks
everyone. I am working on a nested neural network architecture. For the sake of better understanding my question, simply assume the loss is
$L = G(k’) - H(k'')$
where $G$ and $H$ are two functions we ...
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When to know if I am "on the right track" for a CNN architecture
Context
Very new to CNNs and ML in general. I am building a simple binary image segmentation network for generating black and white image masks (white pixels = desired object; black pixels = all else)....
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kerascv retinanet for gender identification
I am using KerasCV Retinanet to detect people and their genders in images.
I would like to detect "man", "woman", "boy", "girl" and "baby" in images. ...
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Unclear steps in derivation of normal equations in linear regression using linear algebra approach
How are eqs.(3.55) and (3.56) obtained? Especially, it is unclear how triangle inequality implies eq.(3.56) because we have squared norms.
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Using Variable Text Features in Machine Learning Model
I am building a classification model using deidentified patient data with ICD-10 codes as inputs. Each code is a string and represents a diagnosis, and these follow the pattern of 1 letter, followed ...
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In the conditional GAN (cGAN) architecture, why does the discriminator need conditional variable?
I'm reading about conditional GAN (cGAN) architecture, what I know is that the generator creates images combining both noise vector and conditional variable, the noise vector brings in random elements ...
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Why can we have misclassifications for a perfect model in logistic regression?
I am reading the book: MACHINE LEARNING- A First Course for Engineers and Scientists, by Lindholm et.al.
Chapter 3, page 50.
Link: http://smlbook.org/book/sml-book-draft-latest.pdf
Consider the ...
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Why is my agent stuck on the same action in my Twin Delayed Deep Deterministic Policy Gradient (TD3) program?
I've been tirelessly converting a reinforcement learning program from Python to JavaScript using TensorFlow.js that is running Twin Delayed Deep Deterministic Policy Gradient (TD3). I'm just trying to ...
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Regression loss conditioned by the ground-truth values
I'm working on a regression problem with a CNN in which the input is a single image, and the output is an angle in degrees (which determines a specific measure related to the image).
Sometimes, the ...
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Neural network for game
There is a game for two, which is an NxN field (always the same size). Players take turns. The first player's goal is to connect the two points (not necessarily at the corners) given on this field. ...
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If the unigram precision is (N-1)/N, then the bigram precision is :
Consider the following machine translation scenario. The reference translation has N words (do not consider sentence beginner ‘hat’ and sentence finisher ‘dot’). The machine output also has N words. ...
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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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Generator loss not decreasing while training GAN
I’ve been attempting to create a basic GAN to generate images using this database of flowers (https://www.robots.ox.ac.uk/~vgg/data/flowers/102/).
I’ve spent a few days on this, and largely based my ...
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I'm trying to build image search like Google Photo-Image with face is given to model & it'll get all the images in database in which he/she is present
When a user upload a selfie, the model search same person in dataset of images of multiple persons and get back all the images in which that person is present.
Step 1: From dataset of images I detect ...
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Classes definition for detecting impervious surfaces on aerial photographies
My project is to use deep learning, essentially a UNET segmentation model, to detect impervious surfaces on high resolution aerial photographies.
I wonder if it's better to train the model with many ...
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What number classes makes a classification problem continuous
I am working on a classification problem, where I have sequences of images and I want to train a model to predict the index of the image with some wanted property.
The target classes would obviously ...
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Feature vector representation of probability distribution
I have a series of multiple probability distribution like this:
[
[0.2, 0.3, 0.5],
[0.1, 0.2, 0.7],
...
]
Do you have any suggestions how I can represent this ...
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Why can't one get extremely high accuracy with an machine learning algorithm?
Suppose we have a classical machine learning problem. Say $m$ training examples and $n$ features with $m >> n$. Suppose I find a great algorithm using automl or otherwise that gives 95% accuracy ...
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Can manual feature extraction be considered a part of a learning algorithm?
A learning algorithm is a tuple $(\mathcal{H}, \mathcal{O}, \mathcal{L})$ where $\mathcal{H}$, $\mathcal{O}$ and $\mathcal{L}$ are the hypothesis class, optimizer and loss function respectively.
We ...
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How can a RNN with 256 cells accept a input of any size?
I built a 3 layered RNN model with 256 cells each using torch. Input feature size is set to 40. Below give a basic Idea on the model.
...
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ML predicting output based on single feature
I am a beginner in ML, I came across the below scenario. I wonder if I could use ML (and which kind) and what would be the the steps needed for the pre-processing of a time-series. So first (1)how to ...
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I have n-dimensional latent representational data, with a y logit label: How do I find peaks in the data using the label?
I essentially need a "find peaks" algorithm for when the input data is n-dimensional.
Specifically, in the latent space of my neural network I have have collected all the training data ...
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How (or can) you formulate the Fisher information matrix in terms of a loss function, specifically cross-entropy loss?
I recently saw the following formulation of the Fisher information matrix in a paper on Transformer pruning:
$$
\mathcal{I} := \frac{1}{|D|} \sum_{(x,y) \in D} \left( \frac{\partial \mathcal{L}(x,y;1)}...
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In the figure of Stable Diffusion, when does the switch part change?
In the illustration of Stable Diffusion, there is a concatenation part through Cross-Attention. Why is there a switch in the concatenation part?
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How to interpret Tom Mitchell's definition of machine learning?
I quote the well known definition:
A computer program is said to learn from experience E with respect to some class of tasks T and some performance measure P, if its performance on T, as measured by ...
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