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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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. ...
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CCN BASE HEART DISEASE PREDICTION USING CARDIAC MRI IMAGES [closed]
AM using cardiac MRI Images to predict heart disease using convolutional neural network.i got confused how to go about it. pls help me.
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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 ...
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Draw the decision boundary of an neural network
I'm really confused when it comes to what the difference between bias and threshold is. I have read that they are basically the same thing and that they serve the same purpose. Being on opposite sides ...
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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 ...
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How to get started with computational research after a PhD in Physics?
I have a PhD in theoretical physics, where I had worked on mathematical models of hydrodynamics around compact objects. I have basic knowledge of programming in C/C++ and Python. Currently, I am more ...
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How sparsity term in loss function for sparse autoencoder is making hidden units inactive?
I am working on a Sparse Autoencoder but Andrew NG's notes are hard to understand.
My question is about the following equation: Loss Function.
In sparse autoencoder, the goal is to inactive some ...
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Dynamic Bandwidth Parameter for Locally Weighted Linear Regression (LWLR)
When performing LWLR we set the weight based on
$w^{(i)} = e^{\dfrac{(x^{(i)}-x)^2}{2\tau^2}}$
where $x$ is the input for our prediction model.
Given two x's - $x_1 and x_2$ s.t. $x_1 != x_2$ and $| ...
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Does ChatGPT use specific sub-programs
I have had a somewhat hard time trying to understand how ChatGPT can "solve" some tasks that cannot be entirely cast as language-model-based rephrasing of textual subsets of the internet ...
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What are the 175 billion parameters used in the GPT-3 language model?
I am currently working my way through Language Models are Few-Shot Learners
, the initial 75-page paper about GPT-3, the language learning model spawning off into ChatGTP.
In it, they mention several ...
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Weighted-Graph Datasets
I am searching for datasets to evaluate an algorithm designed for tasks such as node-classification (edge-prediction, etc.) on weighted and potentially directed graphs.
The Stanford Network Analysis ...
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Matching to Optimise HOTA
I'm trying to understand HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking and in particular the section "Matching to Optimise HOTA" on page 8.
There is a paragraph that says:
...
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System Identification vs Machine Learning for dynamic system modelling
So I found another discussion regarding this, but the answers did not fully separate the differenced between SID and ML. Hopefully a discussion here can shed some light on some larger differences both ...
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Details about KNN based background segmentation
I build a python script which uses OpenCV implementation of KNN Backgroud Segmentation/Remover.
I know the basics of how KNN works, but not how the background segmentation works.
For the MOG algorithm ...
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Convolutional neural network for non-image data
I am working on a project for the prediction of preeclampsia, and one of the algorithms to be used is the convolutional neural network. I am a beginner in machine learning, and I am struggling to ...
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How do you prove a presence of GAN generated samples being exactly (or almost exactly) identical to real observations?
Suppose that $X\in\mathcal{X}$ is data in abstract space and $Z\in\mathcal{Z}$ is random noise vectors in latent space over a defined prior $P(Z)$ (e.g., Gaussian).
Then, let $G$ and $D$ denote ...
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grouping/clustering of lines using Hough transform
I am new to Hough transform, though I have some basic idea about it. I am currently trying to fit the best line to a cluster of points $\left(x_i, y_i\right), i = 1,2,\cdots , N$, where there are ...
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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 ...
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How can I find a Stable Diffusion program?
Well, I know that I'm going to ask too much.
So, I really want to ask you a totally (powerful (!)) free completely off-line code to generate prompt-based images like midjourney. I want to run with my ...
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What should I look for in 2-year colleges if I want to do compsci (ML) at GT or UGA?
I'm going off to college soon and I'm trying to find an inexpensive (<$8K/year) in-state (Georgia) 2-year college to go to before I hopefully eventually transfer to Georgia Tech (I may find myself ...
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Reinforcement Learning Reward Function for Optimizing Golf Aim?
I read this article, mentioning that either here, or StackOverflow would be the best places to ask generic machine learning questions, however, if the question isn't programming specific with a ...
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What are the set of actions in reinforcement learning?
I'm reading https://ai.stanford.edu/~ang/papers/icml04-apprentice.pdf
In 2. Preliminaries, they claim that the reward function takes a state in S to an action
$$R : S \rightarrow A$$
But in the next ...
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Good Practice : Which VAE latent variable to use in dimensionality reduction
I'm trying to use a VAE (CNN-VAE to be exact) to reduce the dimension of some images I have in a dataset.
I successfully trained my VAE, but now I'm not sure of the latent variable to return.
Should I ...
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Regarding constant * opt approximation in agnostic learning
In standard agnostic learning, we assume that there is a concept class $H\subseteq \{h:\{0,1\}^n\rightarrow \{0,1\}\}$. Given samples from a distribution $D:\{0,1\}^n\times \{0,1\}\rightarrow [0,1]$, ...
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How can I understand the proof of the VC dimension of half-spaces in d-dimensions?
Statement : A half space is set of all points on one side of a linear separator, i.e., a set of the form $\{x \mid w^{T}x \ge t\}$. The VC-dimension of half spaces in $d$-dimensions is at least $d+1$.
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What exactly is meant by the term"model" in machine learning?
I am very confused. What is the meant by the term"model" in machine learning?Is it any equation/expression or any software/program or a vector?? I have tried to explore google but couldn't ...
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Relation between machine learning and computer vision?
What is correct concept in regards to relation between machine learning and computer vision?
As shown red encircled in attached snap, computer vision is a subset of machine learning
or
Is it an ...
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Editing facial features
Photoshop offers some great image processing filters under the term "Face-Aware Liquify".
I want to integrate such techniques into an automated processing system I am currently implementing (...
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Why are restricted Boltzman machines not considered useful?
I've been under the impression that RBM can be used for a variety of applications and a cursory search on google seems to confirm this. But I've heard from multiple people (including a peer reviewer) ...
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How to detect touching on a glass with Object Tracking?
I am working at a Biomedical Center right now and my task is to track both pawns of rats and count the number of times they touch the glass surface.
To elaborate. I have a setup that contains one ...
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What are common ways to distinguish between local minima and long run-time in hyperparamteer optimization
I'm using Bayesian optimization for a pretty cost expensive function in the context of neural networks in order to optimize hyperparameters for the neural net.
Is there a general, quantitative way to ...
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Efficient algorithm for finding linear combination of piecewise functions
While porting an algorithm, I having a bit of a problem with finding an efficient algorithm for finding a linear combination of piecewise functions. The procedure is described in the Table 3 of the ...
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resolving in CDCL
When resolving in Conflict driven clause learning, it is the case that if you resolve a conflicting clause with the reason of the negation of one of his literals, then this results in another ...
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Doubt about Subword Regularization algorithm using Unigram Language Model [Kudo, 2018]
I've gone through the paper
Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates
link: https://arxiv.org/pdf/1804.10959.pdf
The algorithm is as ...
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Are these representations of the Bellman Equation equivalent?
I've found two slightly different Bellman Equations. Are they totally equivalent?
I see the one on the bottom has an s' in the reward. Does this or anything else about the groupings change anything ...
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Is there a good rule of thumb for regularization constants?
I'm trying to train some linear logistic regression models and I need regularization. My models contain around 4000 features.
I know that without regularization, a good rule of thumb is to have 10x ...
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Where should I start for making an AI Bot that processes on screen prompts
I would love to learn visual AI and what better way than creating a bot to play offline games. The problem is with no experience in AI other than Azure through my college, I have no experience.
Does ...
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On the bounds of number of iterations of generative adversarial networks
I've been wondering whether there is any analysis on the bounds of number of iterations of generative adversarial networks (GAN), where here 1 iteration refers to 1 step of generator + discriminator. ...
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Why is LSTM hidden state called hidden?
If I undersand correctly, in a LSTM, the output is the concatenation of the consecutives hidden states
$\text{output} = concat_{1\leq ï\leq n}(h_i)$.
But then why are those hidden states $h_i$ called &...
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Learner Algorithm Time & Sample Complexity
Let $X=R^{2}$. Let $u=\left(\frac{\sqrt{3}}{2},-\frac{1}{2}\right),\
w=\left(-\frac{\sqrt{3}}{2},-\frac{1}{2}\right),\ v=\left(0,1\right)$
and $C=H=\left\{h\left(r\right)=\left\{\left(x_{1},x_{2\ }\...
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Cosine distance in a space, and cheating?
I read an example of using cosine distance in RGB space, and it pointed out that (eg.) dark red and light red have a cosine distance (CD) of zero because CD only gives you the angle between vectors ...
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pseudo dimension of the minimum of functions
Suppose a real-valued function class $\mathcal{F}$ with pseudo dimension less than $d$, I am wondering what is the pseudo dimension of the following function class
\begin{equation}
\mathcal{F}_2 = \{\...
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Sensitivity analysis of Multi-Layer Perceptron Neural Networks
I have used Multi-Layer Perceptron Neural Networks to do a binary classification. Now I want to perform a sensitivity analysis. I am planning to follow a similar approach to this paper.
In case the ...
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Calculate the joint probability of two nodes in Markov Random Field
I have a Markov Random Field (MRF) model as described in this picture.
I know the distribution factorizing over the graph is given by $$ p(x_{1:N}) = \frac{1}{Z} \prod\limits_{n=2}^{N} \psi_{n-1, n}(...
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How many model parameters do R-CNN, Fast R-CNN and Faster R-CNN have respectively?
I am making research on object detection and I would like to know the number of parameters these three models obtain.
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K-Modes Clustering with Partially-Overlapping, Variable-Length Data?
I'm working on a project that's attempting to cluster books using machine learning. I'm using the K-Prototypes algorithm for clustering data that has both numerical and class-based data. Under the ...
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How much faster would a semantic segmentation model be with just 2 classes compared to 100 classes?
Let's say I have a semantic segmentation model that distinguishes between 100 classes of objects, and the speed of running the model is 1 image per second.
Now let's say I take the same model ...
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When to stop training a back propagation algorithm based on the MSE?
I have a fully connected neural network that consists of 3 inputs + bias, 4 neuron hidden layer, and 2 layer output, I am using the sigmoid activation function on the hidden layer only and use the ...
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Blackwell-Rao applications to Machine Learning and AI
We learned recently in a graduate statistic course about Blackwell-Rao of an unbiased estimator statistic T; other methods for parameter estimation includes Maximum Likelihood and Method of Moments ...
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Multimodal machine learning dataset fine tuning for binary classification problem
Hello i am new to machine learning, i am working on a multimodal dataset, basically memes dataset, The data consists of an image and an overlay text, i have no clue how to feed a model both of the ...
