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.

Filter by
Sorted by
Tagged with
2 votes
0 answers
130 views

Variational loss of hp-Variational Physics Informed Neural Networks for 2D-Poisson Equation in Tensorflow

I am trying to reproduce the results from the hp-VPINN paper (https://arxiv.org/pdf/2003.05385.pdf) on tensorflow (v1) for Poisson's equation, particularly the two-dimensional Poisson equation. In one ...
user avatar
  • 21
4 votes
0 answers
80 views

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 ...
user avatar
  • 41
10 votes
1 answer
1k views

Sensitivity of $y$ w.r.t. to $x$ in $y=f(x)$ where f is a routine

Given a model $f$ as a programming routine, such that we are able to compute $y=f(x)$ for any $x \in \mathcal{D}$, I am interested in the sensitivity (or let us say derivative) of $y$ with respect to $...
user avatar
  • 244
0 votes
0 answers
137 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 ...
user avatar
0 votes
1 answer
147 views

What are the benefits of using machine learning for interpolation over traditional interpolation methods?

I am trying to get a better understanding of the application of function approximation with machine learning. My question is simple, how does function approximation with ML compare to traditional ...
user avatar
  • 27
1 vote
1 answer
159 views

Improving function approximation with neural network

I am building a neural network to approximate a data set which takes 3 inputs and gives 1 output. After testing the network using a few different iterations of hidden layers and adjusting optimizers ...
user avatar
  • 27
1 vote
0 answers
48 views

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 ...
user avatar
  • 111
4 votes
3 answers
284 views

Advantage of diagonal "jitter" for numerical stability?

In a machine learning code, that computes optimum parameters $\theta _{MLE}$ of a linear regression model, by maximum likelihood estimation: $$ \boldsymbol \theta^\text{ML} = (\boldsymbol\Phi^T\...
user avatar
  • 284
2 votes
1 answer
57 views

How to filter customer voice from customer - agent conversation recordings?

I have a doubt on the project I'm working now. Actually I want only customer voice from the recordings which contains customer-agent conversation.But I have no idea to filter customer voice from ...
user avatar
  • 21
1 vote
1 answer
627 views

How to calculate the number of floating point operations a task/ process requires? (not FLOP/s, but FLOP)

There have been many papers quoting FLOP to quote the performance of a specific approach in machine learning. For example, We trained two models with different capacities: BlazePose Full (6.9 MFlop, ...
user avatar
2 votes
1 answer
109 views

Is there a way to return a substring of a string using Convolutional Neural Networks?

I'm a PhD student in genetics and molecular biology working on an algorithm to identify if a DNA sequence is either a transposable element (TE) or not a TE using convolutional neural networks, and it'...
user avatar
3 votes
2 answers
225 views

Optimization of expensive model with many parameters

I have a physical model which takes $\sim50$ parameters and gives $\sim2000$ outputs taking tens of minutes to run. I need to optimize these parameters to give outputs as close as possible to data. ...
user avatar
  • 131
1 vote
0 answers
29 views

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 ...
user avatar
  • 21
1 vote
1 answer
125 views

Artificial Intelligence, Modeling and Simulation

Artificial Intelligence (AI) and its subsets i.e. Deep Learning (DL), Machine Learning (ML) etc. are becoming more and more ubiquitous in engineering, technology and science. Modeling and Simulation (...
user avatar
  • 418
2 votes
1 answer
127 views

Machine Learning for Optimization

I have a function which takes 100+ coefficients and outputs $x$. I wish to optimise $x$. Running the simulation 50 000 times will take around 15 minutes, however, this happens in parallel - and the ...
user avatar
  • 123
0 votes
0 answers
51 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)...
user avatar
-1 votes
2 answers
71 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 ...
user avatar
1 vote
0 answers
30 views

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, ...
user avatar
0 votes
0 answers
69 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{...
user avatar
  • 21
2 votes
0 answers
136 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 ...
user avatar
  • 21
9 votes
1 answer
4k views

When training a neural network, why choose Adam over L-BGFS for the optimizer?

More specifically, when training a neural network, what reasons are there for choosing an optimizer from the family consisting of stochastic gradient descent (SGD) and its extensions (RMSProp, Adam, ...
user avatar
5 votes
3 answers
259 views

Deep learning using Distributed linear algebra

Is there any deep learning library based on Trillinos or Petsc linear algebra?
user avatar
1 vote
0 answers
85 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 ...
user avatar
  • 31
2 votes
1 answer
23 views

How To Interpret PCA Points Labeled With Specific Data Dimensions

I've done some PCA on my own, and am familiar with the basic concepts of how PCA components are calculated and applied. However, I'm working on a research project and am confused as to how to ...
user avatar
3 votes
0 answers
116 views

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_{\...
user avatar
  • 141
1 vote
0 answers
21 views

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 ...
user avatar
  • 33
2 votes
1 answer
72 views

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: ...
user avatar
2 votes
2 answers
1k views

Interpolation vs. Neural network

I am seeking knowledge from the community. I am solving a transport PDE (conservation of solute mass) using COMSOL. At each Newton-Raphson iteration, I need to update a constant called $Kd$ for some ...
user avatar
  • 65
0 votes
1 answer
52 views

Training accuracy improves but test set accuracy remains the same

I have built an ANN model with 5 hidden layers and 100 nodes in each layer to solve a multilabel classification problem. After the first run, I get a training accuracy of ~66% and a test set accuracy ...
user avatar
1 vote
0 answers
52 views

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 ...
user avatar
  • 11
1 vote
0 answers
106 views

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 ...
user avatar
1 vote
1 answer
30 views

Want to make sense of array dimensions in logistic regression algorithms

I am trying to implement a simple logistic regression algorithm from scratch in python (for learning purposes). Every article I've seen online so far presents the following expression for $z$ (...
user avatar
  • 241
2 votes
1 answer
129 views

Artificial neural networks for Temperature prediction

Imagine I want to consider the temperature for a process given several input varibales. The temperature can be anywhere between 400 and 500 K. Consider I have experimental data to train the network ...
user avatar
  • 33
3 votes
0 answers
104 views

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,...
user avatar
1 vote
3 answers
243 views

Optimization of a blackbox function

Let's say that we have an objective function $f(\mathbf x,\mathbf y)$ which has the parameters $\mathbf x=[x_1\ldots x_n]$ and $\mathbf y=[y_1\ldots y_n]$. Here, $\mathbf y$ is a blackbox variable ...
user avatar
1 vote
0 answers
46 views

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 ...
user avatar
  • 111
1 vote
0 answers
98 views

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 ...
user avatar
  • 111
1 vote
1 answer
226 views

Why is FLOP(Floating Point Operations Per Second) mentioned as a specification on every GPU?

Counting FLOP may not be representative of the actual algorithm real world performance but still all the GPU manufacturers mention FLOPS as a metric of Performance on GPU. Is there any way that this ...
user avatar
4 votes
1 answer
160 views

Using the PAST algorithm to find eigenvectors

I'm working on trying to extract the eigenvectors from a series of observations from a random variable, by using the PAST algorithm, see e.g. 6.2.3 in this book: Large pdf. I don't understand the ...
user avatar
  • 41
1 vote
0 answers
52 views

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,...
user avatar
  • 11
0 votes
2 answers
256 views

Is there any scalable machine learning tool like molecular dynamics software NAMD?

I work in Molecular dynamics of biomolecules. I am familiar with the software NAMD. We use NAMD in our CPU cluster with the aid of mpirun. Without GPUs can I implement large scale deep learning? ...
user avatar
2 votes
1 answer
129 views

Why can't we just use machine learning to select which model to use for a given model?

Suppose you have a classification problem, now what if I implement and train all classification models like logistic regression, KKN, naive Bayes, decision tree or random forest on the training data ...
user avatar
2 votes
2 answers
123 views

Using low rank property for maximal/minimal value search (or sorting)

I was thinking about the following problem: Suppose there is a positive semidefinite matrix $X$ of size $n$ (for example, a kernel). Suppose $X$ can be approximated as a low rank matrix, $X\approx ...
user avatar
  • 372
1 vote
0 answers
50 views

The right algorithm to predict from selected data samples [closed]

I'm trying to come up with the right algorithm for a system in which the user enters a few symptoms and the system has to predict or determine the likelihood that a few selected symptoms are ...
user avatar
1 vote
0 answers
15 views

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, ...
user avatar
5 votes
4 answers
247 views

Learning computational science through guided discovery

I am currently trying to get through Pattern Classification by Duda et al (for a course). However, the book seems too dense for me. Pattern recognition seems like a topic that could be better learned ...
user avatar
  • 192
1 vote
0 answers
274 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 ...
user avatar
0 votes
2 answers
169 views

Machine Learning and predictive maintenance

I am looking for a paper or return on experience regarding the use of Machine Learning in predictive maintenance in the context of data center?
user avatar
0 votes
2 answers
42 views

How are the outcomes that generated from different predictive models combined to get more accurate predictions?

The simple average is commonly used to combine the predictions out of different predictive models. Apart form the simple average, what are the other methods that can be used for combining the ...
user avatar
  • 111
1 vote
0 answers
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 ...
user avatar
  • 111