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Both semi-supervised and self-supervised methods are similar in the sense that the goal is to learn with fewer labels per class. The way both formulate this is quite different:
Self-Supervised Learning:
This line of work aims to learn image representations without requiring human-annotated labels and then use those learned representations on some ...
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In reinforcement learning, exploration has a specific meaning, which is in contrast with the meaning of exploitation, hence the so-called exploration-exploitation dilemma (or trade-off). You explore when you decide to visit states that you have not yet visited or to take actions you have not yet taken. On the other hand, you exploit when you decide to take ...
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Reward in reinforcement learning (RL) is entirely different from a supervised learning (SL) label, but can be related to it indirectly.
In a RL control setting, you can imagine that you had a data oracle that gave you SL training example and label pairs $x_i, y_i$ where $x_i$ represents a state and $y_i$ represents the correct action to take in that state in ...
3
In a nutshell : Memorizing is not Learning
So, first let's just remind the classical use of a neural net, in Supervised Learning :
You have a set of $(x_{train}, y_{train}) \in X \times Y$ pairs, and you want to extract a general mapping law from $X$ to $Y$
You use a neural net function $f_{\theta} : x \rightarrow f_{\theta}(x)$, with $\theta$ the weights (...
3
Supervised learning
The supervised learning (SL) problem is formulated as follows.
You are given a dataset $\mathcal{D} = \{(x_i, y_i)_{i=1}^N$, which is assumed to be drawn i.i.d. from an unknown joint probability distribution $p(x, y)$, where $x_i$ represents the $i$th input and $y_i$ is the corresponding label. You choose a loss function $\mathcal{L}: ...
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The programmer already guides the RL algorithm (or agent) by specifying the reward function. However, the reward function alone may not be sufficient to learn efficiently and fast, as you correctly noticed.
To attempt to solve this inefficiency problem, one solution is to combine reinforcement learning with supervised learning. For example, the paper Deep Q-...
2
It's perfectly reasonable to apply 'traditional' Deep Learning approaches to try and learn an adjacency matrix (a matrix is just a vector of vectors, which can be flattened into a single output vector) but you might need a lot of training data as N gets larger.
Your outputs could certainly have the form of an adjacency matrix, as you describe. Whether it's ...
2
This is the definition of conditional probability + Total probability decomposition formula:
$p(y|x) = \frac{p(y,x}{p(x)} = \frac{p(x,y)}{\sum_{y'}p(x,y')}$.
The idea is to use some unsupervised learning algorithm to learn the distribution $p(x,y)$ for every possible value of $y$, and by using the previous formula you can find $p(y|x)$.
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This formulation/interpretation can indeed be confusing (or even misleading), as the output of a neural network is usually deterministic (i.e. given the same input $x$, the output is always the same, so there is no sampling), and there isn't really a probability distribution that models any uncertainty associated with the parameters of the network or the ...
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Here are two very related interesting papers:
Learning from Human Preferences
Improving Reinforcement Learning with Human Input
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In Supervised learning, the goal is to learn a mapping from points in a feature space to labels. So that for any new input data point, we are able to predict its label. whereas in Unsupervised learning data set is composed only of points in a feature space, i.e. there are no labels & here the goal is to learn some inner structure or organization in the ...
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The equation you are referring to is called Mean Squared Error (or $L_2$ loss) and it is used for regression tasks, where the goal is to predict a real value given some input.
In your case, the inputs are measurements of temperature $y$, either at a certain point in time or point in space or both or none, this is not clear from the image. Now, the goal would ...
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The neural network will learn what we teach it, for example with that image only, after finish training, your model will hard to recognize humans with dark skin, glasses, big eyes, etc, the features that two annotated targets don't have.
If your data is big enough, and contain all the feature of humans face, the result should be good.
If not, I recommend a ...
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How can I generate the target label from the other data in the dataset?
If you are asking how you can create the learning signal in SSL, when given an unlabelled dataset, for learning representations of these unlabelled data, then there is no general answer. The answer depends on the type of data that you have (which can be e.g. textual or visual), and ...
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The main difference between distant supervision (as described in the link you provided) and self-supervision lies on the task the network is trained on.
Distant supervision focuses on generating weak labels for the very same task that would be tackled with supervised labels, and the final result could be directly used for that matter.
Self-supervision is a ...
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Not familiar with RBF but when you have a small data set image augmentation can help. You can do this easily with the Keras ImageDataGenerator, documentation is here. Alternatively you can create image augmentation yourself using image processing models like PIL or CV2.
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Sorry for the delay.
The term "vector-valued feedback" is compared to scalar-valued feedback. The implication (which I should have made explicit) is that, because vector-valued feedback tells the network the correct answer, the changes in weights required to improve performance are reasonably easy to calculate (e.g. using backprop).
In contrast, if a ...
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