Questions tagged [unsupervised-learning]
For questions about AI that learns without being provided with a set of labels (expected answers) along with the set of input examples.
136 questions
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What is the difference between self-supervised and unsupervised learning?
What is the difference between self-supervised and unsupervised learning? The terms logically overlap (and maybe self-supervised learning is a subset of unsupervised learning?), but I cannot pinpoint ...
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Which machine learning algorithm can be used to identify patterns in a dataset of the cache performance of a CPU?
I need a machine learning algorithm to identify patterns in a dataset (saved in a CSV file) that contains details of the cache performance of a CPU. More specifically, the dataset contains columns ...
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What are the different approaches used in Machine Learning?
There seem to be so many sub-fields, so I'm interested in getting a better understanding of the approaches.
I'm looking for information on a single framework per answer, in order to allow for ...
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What is the relationship between these two taxonomies for machine learning with neural networks?
Could you please let me know which of the following classification of Neural Network's learning algorithm is correct?
The first one classifies it into:
supervised,
unsupervised and
reinforcement ...
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Which unsupervised learning technique can be used for anomaly detection in a time series?
I've started working on anomaly detection in Python. My dataset is a time series one. The data is being collected by some sensors which record and collect data on semiconductor-making machines.
My ...
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Do GANs come under supervised learning or unsupervised learning?
Do GANs come under supervised learning or unsupervised learning?
My guess is that they come under supervised learning, as we have labeled dataset of images, but I am not sure as there might be other ...
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How does the network know which objects to track in the paper "Label-Free Supervision of Neural Networks with Physics and Domain Knowledge"?
I was reading the paper Label-Free Supervision of Neural Networks with Physics and Domain Knowledge, published at AAAI 2017, which won the best paper award.
I understand the math and it makes sense. ...
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3
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Has anybody tried unsupervised deep learning from youtube videos?
YouTube has a huge amount of videos, many of which also containing various spoken languages. This would presumably provide something like the data that a "challenged" baby would experience - "...
- 284
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What is the “Hello World” problem of Unsupervised Learning?
As a followup to this question, I'm interested in what the typical "Hello World" problem (first easy example problem) is for unsupervised learning.
A quick Google search didn't find any ...
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Is there a machine learning algorithm to find similar sales patterns?
I have a dataset as follows
(and the table extends to include an extra 146 columns for companies 4-149)
Is there an algorithm I could use effectively to find similar patterns in sales from the other ...
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How does an unsupervised learning model learn?
How does an unsupervised learning model learn, if it does not involve any target values?
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What is the calcium equivalent role in neural networks
I understand that neural networks model biological neurons. Each node in the network represents a neuron cell and the connections between nodes represent the connections between cells. As in nature, ...
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Why do Decision Tree Learning Algorithm preferably outputs the smallest Decision Tree?
I have been following the ML course by Tom Mitchel.
The inherent assumption while using Decision Tree Learning Algo is: The algo. preferably chooses a Decision Tree which is the smallest.
Why is ...
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Is there a way to perform pattern recognition without a labeled training set?
I have a 10GB file of a time series 1D signal. I want to find some patterns within this signal, I know CNN's are great for this but the problem is I don't have any training data.
Now, I could, of ...
- 153
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Is it possible to write an adaptive parser?
I am working on a js library which focuses on error handling. A part of the lib is a stack parser which I'd like to work in most of the environments.
The hard part that there is no standard way to ...
- 159
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What makes learned feature detectors specialize in CNN?
It has been shown that it is possible to use unsupervised learning techniques to produce good feature detectors in CNNs. I can't understand what drives specialization of those feature detectors. In ...
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NEAT can't solve XOR completely
I'm currently implementing the NEAT algorithm. But problems occur when testing it with problems which don't have a linear solution(for example xor). My xor only produces 3 correct outputs once at a ...
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How can reinforcement learning be unsupervised learning if it uses deep learning?
I was watching a video in my online course where I'm learning about A.I. I am a very beginner in it.
At one point in the course, the instructor says that reinforcement learning (RL) needs a deep ...
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Which metric should I use to assess the quality of the clusters?
I have a model that outputs a latent N-dimensional embedding for all data points, trained in a way that clusters data-points from the same class together, while being separated from other clusters ...
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1
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Which unsupervised learning algorithm can be used for peaks detection?
So, I have a dataset that has around 1388 unique products and I have to do unsupervised learning on them in order to find anomalies (high/low peaks).
The data below just represents one product. The <...
- 225
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Which Reinforcement Learning algorithms are efficient for episodic problems?
I have some episodic datasets extracted from a turn-based RTS game in which the current actions leading to the next state doesn’t determine the final solution/outcome of the episode.
The learning is ...
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How can a neural network work with continuous time?
I have an ANN model that receives an input and produces an output. The output is an action that interacts with the environment and changes the input accordingly. The network has a desired environment ...
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Variational Autoencoder task for better feature extraction
I have a CNN with the regression task of a single scalar.
I was wondering if an additional task of reconstructing the image (used for learning visual concepts), seen in a DeepMind presentation with ...
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Why does unsupervised pre-training help in deep learning?
What is the effectiveness of pre-training of unsupervised deep learning?
Does unsupervised deep learning actually work?
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What is the relation between self-taught learning and transfer learning?
I am new to transfer learning and I start by reading A Survey on Transfer Learning, and it stated the following:
according to different situations of labeled and unlabeled data in the source domain, ...
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Are there methods that allow deep networks to learn object categorization in a self-supervised way?
When training a deep network to learn object classification from a set like ImageNet, we minimize the cross entropy between the ground truth and the predicted categories. This is done in a supervised ...
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Why do we need learning in unsupervised learning? [duplicate]
I am not clear with the concept that an unsupervised model learns. We are giving an input and output to the supervised model, so that it can generate a particular value, pattern or something out of it ...
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How could an alien probe learn the basics of a language with only broadcasting signals?
While conducting research, I recently stumbled upon the deep learning and natural language processing concepts. In this question they say that the "grammar induction" is a "supervised ...
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What is graph clustering?
There are several (family of) algorithms that can be used to cluster a set of $d$-dimensional points: for example, k-means, k-medoids, hierarchical clustering (agglomerative or divisive).
What is ...
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How can I cluster this data frame with several features and observations?
How can I cluster the data frame below with several features and observations? And how would I go about determining the quality of those clusters? Is k-NN appropriate for this?
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Is there a bias-variance equivalent in unsupervised learning?
In supervised learning, bias, variance are pretty easy to calculate with labeled data. I was wondering if there's something equivalent in unsupervised learning, or like a way to estimate such things?
...
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How to do machine translation with no labeled data?
Is it be possible to train a neural network, with no parallel bilingual data, for machine translation?
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In unsupervised learning, what is meant by "finding the probability of an image"?
The specific problem I'm having is with a Fully Visible Belief Network. It is an explicit density model (though I don't know what quantifies something being such) that uses the chain rule to decompose ...
- 1,406
3
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1
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What needs to be done to make a fair algorithm?
What needs to be done to make a fair algorithm (supervised and unsupervised)?
In this context, there is no consensus on the definition of fairness, so you can use the definition you find most ...
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Do the eigenvectors represent the original features?
I've got a test dataset with 4 features and the PCA produces a set of 4 eigenvectors, e.g.,
...
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Which unsupervised anomaly detection algorithms are there?
I need to create model which will find suspicious entries or anomalies in a network, whose characteristics or features are the asset_id, ...
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How to implement a Continuous Control of a quadruped robot with Deep Reinforcement Learning in Pybullet and OpenAI Gym?
Description
I have designed this robot in URDF format and its environment in pybullet. Each leg has a minimum and maximum value of movement.
What reinforcement algorithm will be best to create a ...
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Given a set of images that are not divided into groups, which algorithm should I use to do that?
I'm a complete newbie to NNs, and I need your advice.
I have a set of images of symbols, and my goal is to categorize and divide them into groups of symbols that look alike. Without teaching NN ...
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Is there a notion of generalization in unsupervised learning?
I've been learning a little bit about generalization theory, and in particular, the PAC (and PAC-Bayes) approach to thinking about this problem.
So, I started to wonder if there is an analogous ...
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Unsupervised learning to optimize a function of the input [closed]
I am looking to build a neural network that takes an input vector $\mathbf{x}$ and outputs a vector $\mathbf{y}$ such at $f(\mathbf{x}, \mathbf{y})$ is minimized, where $f$ is some function. The ...
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Is unsupervised disentanglement really impossible?
In Locatello et al's Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations he claims to prove unsupervised disentanglement is impossible.
His entire claim is ...
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How can auto-encoders compute the reconstruction error for the new data?
Autoencoders are used for unsupervised anomaly detection by first learning the features of the data set with mainly "normal" data points. Then new data can be considered anomalous if the new ...
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What techniques to explore for dynamic clustering of documents (emails)?
I have a dataset of unlabelled emails that fall into distinct categories (around a dozen). I want to be able to classify them along with new ones to come in the future in a dynamic matter. I know that ...
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Is it normal that SOM clusters the instances with the "versicolor" class into multiple different BMUs?
I have trained (with different sizes, learning rates, and epochs) a SOM network to cluster the Iris dataset. The instances associated with the class setosa have ...
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What are examples of good reference books on unsupervised learning?
I am looking for good introductory and advanced books on unsupervised learning. I have already read books like Probabilistic Graphical Models from D. Kholler and Pattern Recognition and Machine ...
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What technique is used for training Large Language Models like GPT?
I'm learning about GenAI, such as GPT (Generative Pretrained Transformer), and I'm particularly interested in understanding the training techniques used for these models.
Deep learning, generally, can ...
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Where can I find an implementation of the wake-sleep algorithm?
I'm looking to build from scratch an implementation of the wake-sleep algorithm, for unsupervised learning with neural networks. I plan on doing this in Python in order to better understand how it ...
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How does dimensionality reduction occur in Self organizing Map (SOM)?
We have n dimension input for SOM and the output 2-D clusters. How does it happen?
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1
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Is unsupervised learning a branch of AI?
From Artificial Intelligence: A Modern Approach, a book by Stuart Russell and Peter Norvig, this is the definition of AI:
We define AI as the study of agents that receive percepts from the ...
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Will artificial super-intelligence evolve to have selfishness inherent in biological systems?
A lot of experts have expressed concerns about evil super intelligence. While their concerns are valid, is it necessary, what are the chances or how the artificial super-intelligence will evolve to ...
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