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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65 views

Approximating Deep Neural Networks (DNNs) with Binarized Neural Networks (BNNs)

I am working currently as a research intern on Binarized Neural Networks where the weights and the activations of the network are binary. The architecture of this type of networks makes them memory ...
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SMO, Random forest and Bayes net algorithms: why does Random forest perform better?

I analyzed a dataset using those 3 different algorithms. As I can see, Random forest performs better in most cases. My dataset is composed of 4000 instances of two classes (class A 2000 elements, ...
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Papers on depth separations in neural networks

I am new to the domain of Machine learning. I have been asked to present a paper related to the mathematics behind the depth separations in Neural Networks (by Itay Safran, Ronen Eldan and Ohad Shamir)...
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Conv Net dimensions misunderstanding?

I was asked this question: Given an image with shape [1,28,28], what will be the shape of the output of a convolution layer with 10 5x5 kernels (filters) without padding? Now, are the shape dimensions ...
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“Reshaping” vs Polymorphism In Data Wrangling

A lot of the work in data science -- machine learning in particular --involves "reshaping" arrays, tables, and other data structures. These data transformations are not really lossy ...
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How does Pointnet's Shared MLP work? [closed]

When reading about Pointnet's Shared MLP, I kept hearing it described as 1x1 convolution, where each point is process separately. During the Shared MLP, I understand that each point's are expanded ...
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1answer
16 views

which neural network is good for predecting the value of strings

I have a dataset that contains some strings. A numeric value is assigned to each string. I want to develop a machine learning (deep learning) model to get a string and predict its value. What neural ...
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Is MDP Considered as The Model-based Value Iteration in/of Reinforcement Learning?

Is MDP Considered as The Model-based Value Iteration in/of Reinforcement Learning? If no, then Reinforcement Learning is all about being Model-free learning. Right?
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Why did Apple include dedicated a neural network “processor” in standard consumer products?

Not sure if this is the right place, but I guess it is better than Reddit and I couldn't find any discussion. I was wondering why Apple include a neural network "processor" and can't help ...
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Monte Carlo tree search optimizations

I am designing a algoritm for a game using a Monte Carlo tree search AI that I implemented. I play against another player and want to get the best move. Every time I do a move I completely build a new ...
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1answer
36 views

What is a task type?

I have an unlabeled dataset: 500 X-rays of seeds in husk. In each image there are different number of seeds, for instance, from $10$ to $50$. A seed has some features. The main features are a seeds' ...
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Backprop formula question

I'm reading this chapter https://www.deeplearningbook.org/contents/mlp.html of the Deep Learning book, and on page 209, they have this equation (assume there is no regularizer and no bias parameter): $...
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61 views

How to do supervised learning without knowing the target

I'm working on an optimization algorithm that I think could be considered machine learning, but I'm not sure. Basically I have a model that I want to optimize by adjusting its parameters. I don't ...
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Dimension Reduction - Which feature should remove to reduce the dimension of the matrix

Let's suppose that we have the following 2 tables: If we want to reduce the dimension by one(in every table) which feature we should remove and why ? I am confused about the way that i should work ...
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29 views

Argmax in $k$-NN algorithm

Given a query instance $x_q$ to be classified Let $x_1,\ldots,x_k$ be $k$ instances which are nearest to $x_q$ $$ \hat{f}(x_q) \gets \operatorname*{argmax}_{v \in V} \sum_{i=1}^k \delta(v,f(x_i)), $$ ...
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Are most machine learning problems the same problem? (NP-hard)

I am trying to teach P vs NP to some primarily Machine Learning folks. I wanted to come up with an introductory fact to grab their attention. Reasoning for Question: Most problems in Machine Learning ...
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Can the perceptron classifier achieve perfect accuracy, on any data set?

I was thinking. Since any data can become linearly seprabale through kernel methods, meaning there is a dimension where this data is linearly seprable, so feed this processed data set into the ...
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Perceptron and non linearly seprable data

I was asked in an interview question, can a perceptron classifier ever reach 100% accuracy on some kind of non linearly seprabale training data in 2D. I said that no it can't because the data is not ...
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88 views

Asymptotic analysis for machine learning algorithms

I wanted to know if it would practical and useful to analyse machine learning algorithms in terms of asymptotic computational complexity. I have noticed this is very uncommon. However, I believe it ...
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1answer
135 views

What does “Temporal extent” mean?

I am reading Long-term Temporal Convolutions for Action Recognition and under the Section 3.1, I read this: To investigate the impact of long-term temporal convolutions, we here study network inputs ...
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Perceptron - Generalization Bounds & Compression Bounds

A distribution $P$ over $\mathbb{R}^{d} \times\{-1,+1\}$ being $(\gamma, R)$ -separable. We now let $\mathcal{P}_{\gamma}$ denote the set of all $(\gamma, 1)$ separable distributions. For a ...
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1answer
30 views

Expressivity of neural networks, how much information can be stored

I want to know whether a given neural network (with a finite number of nodes) is able to store all injective maps f: D -> C, where D has cardinality k and C has cardinality N (so the number of maps ...
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48 views

Method for combining derivative free optimization results of different data inputs

I am working on an algorithm that has multiple fixed parameters. The algorithm analyzes time series data and spits out a number. The fixed parameters need to be such that this number is as small as ...
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76 views

Batch sizing for convolution neural networks — powers of 2 or powers of primes?

The conventional wisdom for convolution neural networks (CNNs) is to make the batch size a power of 2 because of hardware utilization/optimizations done in the convolution layers. A similar logic ...
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Machine learning algorithms for recognising national anthem song of all countries

https://en.wikipedia.org/wiki/National_anthem Are there machine learning algorithms which will recognise and interpret the national anthem song of all countries? Input dataset : National anthem sound ...
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How to predict the number of patients from the number of diagnoses

I have data on the diagnostic measures that individual patients received that year. For that individual data, I would like to make a prediction of how many such patients are in the country. To do so, ...
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1answer
186 views

Pac learnable when changing distribution

if H is Pac learnable according to Definition 3.1 (PAC Learnability) A hypothesis class H is PAC learnable if there exist a function $m_H\colon(0,1)^2 \mapsto N$ and a learning algorithm with the ...
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53 views

What kind of bigram probability smoothing is this?

I hope it isn't off topic but I need to understand this example. Given the corpus 12 1 13 12 15 234 2526 and smoothing factor of ...
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Confused on Variational AutoEncoder

I'm a bit confused on how Variational Autoencoders are trained. In particular I'm confused on how the latent variable is generated for each input. My questions are as follows: When running ...
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22 views

Is machine learning and AI all about pattern recognition?

I realized that machine learning and AI is mostly about spamming the auto correlation and cross correlations from mathematics and combining them with memory arrays. Is it right? What do you think ...
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61 views

Can you share some pointers to get better at working alone in projects?

I am an ML Engineer who works alone in projects most of the time. I don't have people who are much smarter than me working alongside. Most of the time, I feel that working alone is a tedious job,one ...
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If we add a constant weight vector with an absolute function, does it still remain convex then? [closed]

We know that absolute functions are convex. Now what if we add a constant weight vector to it, does it still remain convex? Say the equation is Absolute loss regression + L1 regularization, we know ...
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Machine Learning - learning path

So I'm interested in learning Machine Learning and in specific in the Healthcare domain. But I'm so overwhelmed at the different learning paths the internet holds. Can someone help me out with how I ...
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19 views

How to get a heatmap of pixel influences from a Convolutional Neural Network

If I have a CNN model (let's say I'm using Tensorflow and Keras) that I've trained for a particular task. For example, let's say I've trained it to detect the difference between an apple and banana. ...
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25 views

Is deepfake detection viable?

I'm thinking of doing a project on deepfake detection, but I'm not entirely sure if it is viable. Based on my understanding, how it works is that deepfake generation programs have a generative and ...
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How can machine learning be used in making sure a document has all the required headings and details?

We are trying to build a system that would accept fyp proposal documents and then would validate is there something missing, like a heading or a chart that should be in the document according to the ...
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Perpendicular Training Vectors SVM

Claim: If we are training a hard-margin SVM on a set of perpendicular training vectors which can either be classified as "positive" or "negative," will every training vector end up ...
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1answer
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Signal translation with Seq2Seq model

I'm currently doing some research on signal processing and I got a dataset which includes the signal in itself and its "translation". So I want to use a Many-to-Many RNN to translate the ...
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How is the RKHS norm related to sample complexity or other learning theory properties?

This is a somewhat soft question. Given two reproducing kernel Hilbert spaces (RKHSs) $H_1$ and $H_2$, if their RKHS norms only differ by a constant, i.e., $C_1\|f\|_{H_1}\le \|f\|_{H_2} \le C_2\|f\|_{...
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1answer
42 views

Density of uniform distribution over two disjoint squares

A probability distribution $P$ over $X \times \{0, 1\}$. $P$ can be defined in term of its marginal distribution over $X$ , which we will denote by $P_X$ and the conditional labeling distribution, ...
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37 views

What is a Black Box attack against Machine Learning algorithms?

And is there an attack strategy that you can use to approximate the architecture of a machine learning system with the knowledge of class labels and some data points?
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ML Algorithm for getting top pick in each sample

I have a dataset of streets - and each street contains several houses. ...
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1answer
108 views

Computer Vision Techniques to find Slope of beach in the 2D Images?

What I am planning to do is to calculate the height and estimate the slope of the segmented Object. The camera will be static and the Object of Interest is the slope of the beach. I am finding harder ...
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22 views

What is a good reference for NP hardness in the machine learning/optimization/operations research context?

I am reading some papers in machine learning and at the very beginning (introduction) you will see statements of theorems that says, for example: Theorem 1.1. For any constant ϵ > 0, it is NP-hard ...
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1answer
62 views

Empirical Risk and True Risk - Generalization Error Proof

I showed that, over an uncountable domain,learner A and a distribution P, such that for every sample size m and all samples S from $P^m$ $$ : L_S(A(S)) − L_P (A(S))| = 1 $$ Now I wanna prove for ...
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1answer
30 views

Mapping categorical data in K-nearest neighbour

I have a data set which contains categorical data, for example: ...
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Modifying the code for reading .ogg datasets and apply LSTM

Deep learning/LSTM/Matlab There is a Matlab code that is doing the following steps for deep learning and applying LSTM, I need to change first three steps to use our dataset to train this model and ...
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36 views

Machine Learning method maps from $\mathbb{R}$ to $\mathbb{R}^2$

I have worked with image classification and image segmentation etc. While working with images we are either mapping from $\mathbb{R}^2\to\mathbb{R}$ or $\mathbb{R}^2\to\mathbb{R}^2$. Are there any ...
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What does the decision boundary of XOR problem look like?

My textbook walks through an example of solving the XOR problem in machine learning using a two-dimensional RBF network. It does this by setting the centers for the two basis functions at [0,0] and [1,...
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How to cluster a dataset in which each data point is composed of a set of 2-dimensional coordinates

I have a dataset with totally $1000$ scenarios, each of which is composed of $5$ users' coordinates $(x_i,y_i), \forall i \in \{1,\dots,5\}$. Now, based on users' coordinates, I want to cluster these $...

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