Questions tagged [machine-learning]
Questions about computer algorithms that automatically discover patterns in data and make good decisions based on them.
1,127
questions
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8 views
Finding the disagreement coefficient for certain hypothesis classes and distributions
I need help with the following question:
First some definitions,
Let $\cal X \subseteq \mathbb R^d$ be some example domain and let $\cal H$
be a hypothesis class on a distribution $\cal D_\cal X$ ...
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0answers
12 views
Ratio of intra-class scatter by inter-class scatter is getting minimised by linear discrimination process
I am optimising projection matrix P using Fisher linear discrimination formula
maximise following with P
J=N*tr(trnspse(P).SB.trnspse(SB).P) / c * tr(trnspose(P).SW.transpose(SW).P)
As per theory, P ...
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15 views
A confidence interval algorithm for Disagreement coefficient
My question has to do with the disagreement coefficient in active learning. I've been trying to solve the following question, where I need an algorithm to derive a confidence interval for $\theta$, ...
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0answers
51 views
Could someone explain the algorithm from this paper? (Thank you) [closed]
Trying to get a fair understanding of our artificial immune systems. To do this I’ve been reviewing this paper, but the algorithm and mathematics is over my head, could someone explain the below to me ...
2
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1answer
24 views
What are the job names for researching neural networks & deep learning?
I am a student who wishes to research neural networks (ANN, CNN, RNN) and deep learning, write papers on those topics, and actively participate in forums like CVPR.
My question is:
Which professions ...
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14 views
Can you plug a cmos camera (like the iPhone 8 back camera) into an mpcie in a laptop? [closed]
I read that 12 megapixel iPhone cameras don’t record video in 12 megapixels because the processors are not able to keep up. It seems though, that if I could plug the cmos sensor to the cpu of a laptop,...
3
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1answer
18 views
What is the primary reference for the observation/discussion of how neural networks struggle with ambiguous training datasets?
It is known that neural networks, such as convolutional neural networks, struggle with pattern recognition if training sets contain ambiguities (i.e. several labels can correspond to one and the same ...
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15 views
What is the point of Transforms in Pytorch when loading Dataset?
I am not sure what the point of the data augmentations in PyTorch when loading images. Specifically, I am talking about this:
...
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108 views
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How does the TEPS benchmark work and why is it relevant to real world problems?
Graph500 is a competition for supercomputers that uses a different benchmark "Traversed Edges Per Second", which is supposed to measure some notion of the communication bandwidth ability of ...
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0answers
24 views
Question of about Boltzmann machine implementation
I would like to implement Boltzmann machine with two hidden and two visible units. The four possible hidden units configuration are $(0,0), (0,1), (1,0), (1,1)$, and their probability distribution is $...
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2answers
23 views
Can we supervise on the hidden states of RNN?
I'm trying to generate some history-dependent model with machine learning, whose underline physical model has a clear definition of its "internal state variable" (a state derived from ...
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9 views
Learning algorithms- difference between a learner (the algorithm) having continuous access to the samples (oracle) vs getting all at start
Is there any fundamental difference between learning algorithms e.g. variants of PAC which have continuous access to examples on which to train (i.e. these are obtained as the algorithm runs, when ...
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0answers
13 views
Using Restricted Boltzmann Machines for clustering data
I want to use RBMs as a clustering model and the idea is to use an RBM for clustering a 16 class clustering problem with 4 nodes in the hidden layer. The clustering is done by updating the hidden ...
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0answers
27 views
Describing a consistent learner
Let $X=R^3$.
Let $C=H=\{h(a,b,c)=\{(x,y,z) |x|\leq a,|y|\leq b, |z|\leq c\}, a,b,c\in R_+\}$ the set of all origin centered boxes.
Describe a polynomial sample complexity algorithm that learns $C$ ...
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1answer
75 views
Why does it take O(n!) time to specify a canonical ordering for learning flatten adjacency matrices/graphs?
I was reading a paper for learning graphs (paper is GraphRNN) and it says in section 2.2 (emphasis by me):
Vector-representation based models. One naive approach would be to represent G by flattening ...
1
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1answer
18 views
How many pixels support each neuron in multi-layer CNN?
I'm studying for a computer vision module and I'm on the deep learning topic, in one past paper we have the following question:
Given that a convolutional neural network has five convolution layers (...
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0answers
23 views
Applications of derivative only, zeroth-order free optimization
I understand what is derivative-free optimization, and I am thinking a similar problem where the function $f$ we are optimizing is unknown and the only information we can acquire is the derivative. In ...
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0answers
13 views
What are open-loop and closed-loop modes of neural networks?
I came across the following line in the book ‘Deep Learning (Ian Goodfellow) 10.2.1, pg 374;
The disadvantage of strict teacher forcing arises if the network is going to be later used in an open-loop ...
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1answer
23 views
What is the relation between the compatibile features and the state features in Actor Critic Algorithm?
According to Actor-Critic algorithm, $\psi_{\theta}=\nabla_{\theta}\ln \mu_{\theta}(s, a)$ where $\mu_{\theta}(s, a)$ is the policy followed by the actor and $\psi_\theta$ is the compatibile features ...
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1answer
24 views
Find the exactly correct separating hyperplane of SVM when the data is not perfectly linearly separable
I am thinking about the following case where the data in region 1 is always positive and the data in region 2 is always negative, but the data in region 3 can be both positive and negative. Are there ...
2
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1answer
58 views
How to transform an Abstract Syntax Tree (AST) to an Abstract Binding Tree (ABT)? (for machine learning fo theorem proving)
I was reading the HOList paper that applies Graph Neural Networks (GNNs) to the HOL Light (HOList) data set and benchmark for ML for theorem proving. They describe their results etc but there is no ...
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0answers
20 views
Reference selection for rating assignment based on pair comparison
Background: a set of 300 images are prepared, randomly pair compared with 60 other images, assigned a rating (from 1-star to 5-star) based on the pair comparison score (+1 if wins a pair comparison ...
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0answers
10 views
Sequence prediction with known information
recently Hamas has shot a lot of rockets to Israeli cities. I found a large database containing thousands of entries from the past 3 days.
The database is a large list of triplets in the form of $(...
3
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1answer
33 views
Representing abstract syntax tree as a graph
Does it make sense to represent an AST as a graph? How can one achieve a mapping between ASTs and graphs that preserves both semantic and syntactic properties of source code?
The goal and application ...
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1answer
68 views
Pseudo-dimension of a subset of affine functions
Let's say there are two sets of affine functions.
$\mathcal{A} = \{ax +b \mid a,b \in \mathbb{R}\}$
$\mathcal{H} = \{2x + 1, x, 3x + 4, 4x\}$
I know that the $\mathrm{Pdim}(\mathcal{A}) = 2$. From ...
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0answers
10 views
Reinforcement learning with 0 rewards and costs
Suppose we have a hallway environment, i.e, $N$ nodes from left to right, and we can either move left or right. Moving left at the leftmost node does nothing and reaching the right most node gives you ...
2
votes
1answer
34 views
Uniform convergence for a class of finite dimension
The following theorem is cited in Balcan, M.F., Sandholm, T. and Vitercik, E., 2019. Estimating approximate incentive compatibility which I am currently reading and it is referenced to David Pollard. ...
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1answer
17 views
Compare Hidden Markov Model's sample with ground truth data
I have a time-serie and I fit different HMMs on it, each with a different number of hidden states.
Now after sampling from the models , I'd like to compare the results with the ground truth data and ...
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0answers
40 views
Graph Neural Network models
I recently started self-learning about GNNs, and I have trouble understanding the difference between the GNN models.
As I understand it, all GNN models apply an aggregation step on each node, and the ...
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0answers
20 views
How to get best path from a set of path using Q-Learning?
I have 10 data sets (lat and long) of the same path. I started from point A and stopped in point B and did this 10 times for a single route to get the data. While collecting the data, there were some ...
1
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1answer
108 views
How can I make inference in CompuBERT?
My question is about the paper Three is Better than One Ensembling Math Information Retrieval Systems (a system used for math information retrieval - both for finding answers and formula search)(code ...
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0answers
15 views
interplay between evolutionary algorithms and machine learning from neuroscience
Evolutionary algorithms (EA) being those inspired by the natural evolution (mutation and selection), and Machine Learning (ML) motivated by the neural concepts of human and animal learning (brain's ...
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22 views
How to find the relation between two models, and sort of inductive bias is that is implemented in those models
I am pretty new with data science and Machine Learning. I am learning form one textbook and I found this task. I have no Idea from where to start and what relation could be. Any help would be great.
...
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1answer
63 views
Time complexity of computing $AA^T$
What is the time complexity of computing $AA^T$? The context is building a co-citation weighted adjacency matrix.
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0answers
23 views
Discrete action space for MADDPG
I am trying to apply MADDPG, a policy gradient algorithm that uses centralized training and decentralized execution, to a project. In the work of Lowe et al., the actor returns a pmf for a discrete ...
2
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1answer
11 views
Pruning: Outlier Candidate Selection
I am using method 3.3 described in Outlier Detection using Isolation Forest and Local Outlier
Factor .
It states:
Specify a dataset: $𝐷={𝑑_1, 𝑑_2, ..., 𝑑_𝑛}$.
Here, 𝑛 is the sample number of 𝐷. ...
0
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1answer
24 views
K Nearest Neighbor using K-D Tree (What ratio makes K-D beneficial)?
I'm trying to implement a KNearestNeighbor model and came across the fact that many professional models use a K-D tree to index the K Nearest Neigbors. I also read that high-dimensional data makes a K-...
2
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1answer
31 views
Raising to the T in machine learning
What does it mean when in a machine learning paper there is
$(arg)^{T}$, what does the T does to an arg in this 3b1b video on neural networks he puts the: $(w^{l-1})^{T}$
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90 views
Which features can be considered for neural network based SAT solving?
I'm trying to implement SAT solver, based on backtracking algorithm and BCP. This SAT solver is trying to pick one literal from each clause, from 3-CNF SAT instances. I've implemented a neural network ...
1
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1answer
48 views
PAC learning: success of learning when receiving a sample from a different distribution
I've come across this question and I think I'm on the right track with the idea I just don't really know how to formalize it properly or understand why everything said in the question is required for ...
2
votes
1answer
17 views
Structuring a Regression Model for NCAA Basketball Spreads/Totals
I have a basic linear regression model that predicts the spread and total points of NCAA college basketball games. My inputs include various efficiency metrics about each team playing each other. A ...
0
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0answers
37 views
Choosing unsupervised learning algorithm for analyzing the spectrum of a linear operator
I am a theoretical physicist, and new to CS.stackexchange, and have a little knowledge of CS, and in Machine Learning (only some general stuff). In physics we often analyze the spectrum of linear ...
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2answers
53 views
What computational complexity classification does autonomous/self-driving cars belong to?
Self-driving car technology continues to attract popular attention and interest in today's media, but how would a computer scientist explain the theoretical nature of the problem? For example, we are ...
3
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2answers
159 views
Proving a certain hypothesis class on a given distribution is not learnable
I had this question in Learning theory, but it's really just a question in probability theory to be honest, so I'm gonna try to rephrase it in a way that really emphasizes what I was trying to do to ...
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0answers
35 views
Coordinate descent for Lasso, Question about algorithm
I'm not sure why the algorithm computes $c_k$ with $\sum_{j \neq k} w_j x_{i, j}$. Why does one need to ignore the $k^{th}$ feature here? I'm not sure how this is derived. Is this the result of taking ...
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0answers
15 views
Where can I learn theoretical aspect of Reinforcement learning
Every RL paper has a section with some finite sample analysis/error bound/ convergence proofs. I have difficulty fully understanding such proofs and coming up with my own analysis for my personal ...
3
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1answer
78 views
AdaBoost - why using such alpha function?
I'm reading the paper where AdaBoost was invented (link), and I couldn't understand why they have chosen the formula α_t = 1/2 * ln((1-ε_t) / ε_t).
snippet:
...
1
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1answer
59 views
Could AI be used to detect when a human is picking survey response options randomly?
Context: I am a clinical psych researcher dabbling in machine learning.
Humans cannot be truly random. Therefore, could machine learning be used to analyze a string of numbers and determine the ...
0
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1answer
36 views
Linear Discriminant Method as Pre-processing for inputs going into a Neural Network?
As the title suggests, I am curious whether Linear Discriminant Method can be performed on a dataset as pre-processing before putting the reduced-dimensional data as input for a neural network for ...
0
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1answer
37 views
PCA applicability
I understand that PCA takes a data set with input size, with output labels, and reduces the inputs to a set of principal components size r, where r < n.
My question is whether or not this can be ...
