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Questions tagged [machine-learning]

Machine learning, a branch of artificial intelligence, is the science of getting computer systems to meaningfully act without being explicitly programmed by human.

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SINDy Vs standard methods for system identification

I have been trying to understand the recently proposed Sparse Identification of Nonlinear Dynamics SINDy. Despite several attempts, I seem to fail to understand the difference between SINDy and the ...
Chenna K's user avatar
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Identifying an unknown P.D.E. from solution data

I have a black-box simulation that produces the time evolution of a probability density function p(x, t) in 1 dimension from arbitrary initial conditions p(x, 0). The underlying simulation occurs on a ...
beables's user avatar
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Calculus of Variations with unknown cost function but some data

I have a problem that I've framed out in a particular way, but I don't know if I'm re-inventing the wheel here. Is there an existing literature base in this problem? Does it have a corresponding term ...
Sycorax's user avatar
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A maximization problem, with motivation in machine learning

Consider the minimization problem described this paper. Let $f_{\lambda}$ be the minimizer. As a part of extending my work, I am able to show the following facts $$\lim_\limits{\lambda \to 0}\|f_{\...
Rajesh D's user avatar
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Are the No Free Lunch Theorems Useful for Anything?

I have been thinking about the No Free Lunch (NFL) theorems lately, and I have a question which probably every one who has ever thought of the NFL theorems has also had. I am asking this question here,...
Surgical Commander's user avatar
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Which class of PDEs is well-suited for multistep methods?

I am experimenting with machine learning techniques to solve partial differential equations (PDEs). My goal is to use solutions from previous time steps to predict the solution at the next time step, ...
user572780's user avatar
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0 answers
203 views

Proving convexity of Frobenius norm and correlation function formulations of an optimization problem

I have been working on formulating my requirements in the form of an optimization problem in a multi-output regression setting. Firstly, I would like to make the variables I used in the problem and ...
venkat's user avatar
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scipy.linalg.sparse.eigsh does not work for generalised eigenvalues

I asked this question over at StackOverflow and someone told me that I'd get a better answer here. So here's my problem: I'm working on a machine learning project which involves doing a Principal ...
Ramtin Yazdanian's user avatar
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What is average pooling

I was reading an article where they have a set of pages. For every page they extract a feature vector of dimention 300. Then they fuse these vectors into one vector by using a method called average ...
Hani Gotc's user avatar
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1 answer
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Activation function with special conditions in machine learning

I only have a basic understanding of deep learning, but looking through it I had an idea on how to approximate global minima of the NN. However, for it's activation function I am only able to use: ...
olukatorzu's user avatar
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What architecture of CNN to solving a image regression problem (case study: solving Poisson equation)?

I've been working on solving Poisson problem using CNN model (you can ignore the Poisson problem part if you not familiar and jump to the image processing/CNN part). More specifically, I am solving ...
samueljohlal's user avatar
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Neural networks trained with subspace iteration algorithms for solving SCF eigenvalue problems

I am toying with the idea of using current libraries available with SCF (Self-Consistent Field) subspace iteration codes to train an ANN (Artificial Neural Network) to solve for SCF eigenvalue ...
Jonathan's user avatar
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How property-invariance is imposed to neural nets?

I was wondering how specific symmetries or constraints such as property-invariance transformation are imposed on any (deep) neural net when they are trained. I'll appreciate it if anyone can aware me ...
arash's user avatar
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Why is scoring a separate problem even after docking is done?

With regards to protein-ligand interactions, we talk about two concepts, docking and scoring. I understand that docking means to find the best orientation of a ligand at the active site of a protein, ...
Mahathi Vempati's user avatar
1 vote
0 answers
91 views

Eigenfaces Algorithm

This might be a silly quesntion but recently I've been trying to program the eigenface algorithm using PCA, so I arranged the face vectors vertically in a matrix X such as: X = [x1,x2,x3,...,xn]; In ...
Marcus's user avatar
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Tracking channel states using Machine Learning

I am new in AI and would like to apply machine learning to estimate the channel states. I have a set of data. It is a matrix of 10000*8. Each row of this matrix is regarding a time step, i.e., 1st row ...
Susan's user avatar
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Singular Spectrum Analysis Explanation

I need you to help me understand the Singular Spectrum Analysis algorithm. I already read a lot of articles about the subject but they never answered my questions like what is the mathematical reason ...
Olivier's user avatar
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Connection between piecewise linear basis functions and RELU activation function

ReLU activation is defined as follows $$\sigma(x)=\max(0, x).$$ Let's assume that I have deep network of 1 hidden layer, than output from my layer has form $$ f(x)= \sigma(Wx +b), $$ where matrix W ...
computational_scientist's user avatar
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What is a performant clustering algorithm for approx 10,000 vectors of approx 30 dimension?

I have a set of real-valed vectors, for example $S = \{v_1, v_2, ..., v_k\}$ $v_i = \begin{pmatrix} age_i \\ height_i \\ weight_i \\ ... \end{pmatrix}$ or whatever. Each vector has on the order of ...
spraff's user avatar
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Is there any ML solutions for permutations?

In Machine Learning field, is there any work done, or research, or info in general about finding optimal permutations? For eg. to find a number of optimal arrangements of sliced shapes on 2D sheet of ...
madneon's user avatar
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Update model parameter with new data, discarding old data

I have this dataset, and I am using y = (a * x^n) / (b + x^n) Hill function as the model, where a is the limit of the Hill curve,...
neo4k's user avatar
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Optimizing estimator of composed functions when function is known

Note: This was cross-posted from comp-sci, as I didn't know this community existed! I have a problem which I'm looking to see if there is literature on: Consider three types of actors, a Director, ...
Nate Diamond's user avatar
1 vote
0 answers
305 views

Generalization error and Sample Complexity estimation for Least Squares

I am wondering how to draw a sample complexity plot similar to the following figure which shows the estimated number of samples to incur no more than 10 percent generalisation error on average for the ...
santteegt's user avatar
1 vote
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64 views

How to make the following data separable for the classification into three classes?

The figure below shows the PCA projections of inputs which are 14 meteorological features, (i.e. wind, temperature, humidity, pressure, and so on.) I would like to use any technique to make it more ...
mhdella's user avatar
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How to find active region in treepartition Matlab object

I am trying to identify a non-linear plant using the identification toolbox of Matlab. In particular I would like to identify a non-linear arx model, where the nonlinearities are expressed by means of ...
FancyPants's user avatar
1 vote
0 answers
93 views

How to create synthetic data from known weights

I'm doing some machine learning where I have lots of data and through optimization I'm trying to learn the weights for the model. I'd like to check that my learning actually works correctly. For that ...
siamii's user avatar
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Is the following the correct implementation of VGG network?

As exercise I am implementing few fundamental networks. Specifically right now I am implementing VGG The code I've got at the moment is the following: class MyVGG(nn.Module): ...
user8469759's user avatar
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dual svm square hinge loss

Let $x_1,\dots,x_n\in \mathbb{R}^n$, $y_1,\dots,y_n\in \{-1,1\}$, $\lambda \ge 0$ and $K$ be the invertible Gram matrix $K=(x_i\cdot x_j)_{ij}$. Consider $$ (P) \qquad \qquad \min_{a\in \mathbb{R}^n} \...
Smilia's user avatar
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Nanograd differentation; what is going inside the python code

I am reading up on deep learning and I am trying to understand the backpropagation methods in python nanograd. See; https://github.com/rasmusbergpalm/nanograd This is a method for computing the ...
economist101's user avatar
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Verification of a Function Definition in Python

I want to write a function $f$ and it is defined as $f = - \nabla \cdot(|\nabla u|^{p-2} \nabla u) $ and I exact solution $u(x) = \tilde{u}(r) = 1 - \frac{p-1}{p-2} \left( s^{p/{p-1}} - (1-s)^{p/{p-1}}...
User124356's user avatar
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141 views

Getting euclidean distance between vector A and C without anyway of retrieving them when their distances with a common vector B is known

Motivation: My plan is to get the overall euclidean distance matrix for all the vectors in N number of dataset. Each dataset is basically an array of n-dimensional points. For e.g: A dataset can be ...
Shihab Ullah's user avatar
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0 answers
54 views

Unsupervised event prediction with time series data

I have to solve a problem using unsupervised time series forecast. I have a dataset where three columns are given, 1. Endtime: when the row was generated ( it generates a row after each 54-55 seconds)...
ujjwal anand's user avatar
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96 views

exploding gradients in gradient descent procedure of multi-output ridge regression

Multi-output ridge regression: $$W^{*}=\underset{W}{\arg \min } \frac{1}{\mathcal{N}}\|Y-WX\|_{F}^{2}+\lambda\|W\|_{F}^{2}$$ There are $Q$ outputs, $N$ samples, and $P$ covariates (features). $\hat{...
venkat's user avatar
  • 21
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approximation of nonlinear time-dependent system with history

I have two time-dependent coupled equations. One of which is several orders of magnitude more computationally demanding than the other. I am trying to use machine learning to reproduce the behavior of ...
user1984528's user avatar
-1 votes
2 answers
75 views

How to find the relationship between an independent variable in a time series and a single dependent variable

I have a dataset of crop yield of a seasonal crop, under environmental conditions (rainfall, humidity, temperature etc.). Daily environmental conditions are recorded over few years with the crop yield ...
Dulanga Kithmini's user avatar