Questions tagged [terminology]

For questions related to the definition of and use of terminology in the context of Artificial Intelligence

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Is there a name for this approach to evolutionary algorithms?

I am considering an approach to evolutionary algorithms, in which instead of maintaining a population of individuals, we maintain a pool of $N$ mutations that can be applied to a base genome. For ...
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Is there a clear distinction between Artificial Intelligence and running a sequential program?

Artificial Intelligence (AI) is often defined as a machine that is intelligent, or one that can think rationally. From a high-level perspective, things like self-driving car or Alpha-Go can easily be ...
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When a deep learning paper mentions projections does that mean no bias?

Sometimes a paper mentions a projection layer where some dimensionality is projected onto another to enforce some downstream dimensionality matching requirement. I actually don't have any links at ...
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Origins of the name of convolutional neural networks

Convolutional neural networks (CNNs) contain convolutional layers. In modern deep learning libraries such as Tensorflow and PyTorch among others, convolutional layers are implemented by using the ...
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20 views

What is meant by “real-valued argument” in this context of the convolution operation?

Consider the following statement from Deep Learning book (p. 327, chapter 9: Convolutional Networks) In its most general form, convolution is an operation on two functions of a real-valued argument. ...
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What does “statistical efficiency” mean in this context?

Consider the following statement(s) from Deep Learning book (p. 333, chapter 9: Convolutional Networks) Convolution is thus dramatically more efficient than dense matrix multiplication in terms of ...
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What does “semantic gap” mean?

I was reading DT-LET: Deep transfer learning by exploring where to transfer, and it contains the following: It should be noted direct use of labeled source domain data on a new scene of target domain ...
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Does Yann LeCun consider k-means self-supervised learning?

I was discussing the topic of self-supervised learning with a colleague. After a while we realized we were using different definitions. That's never helpful. Both of us were introduced to self-...
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What part of the Vaswani et al. is the “transformer”?

Which part of this is the transformer? Ok, the caption says the whole thing is the transformer, but that's back in 2017 when the paper was published. My question is about how the community uses the ...
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Is the bias also a “weight” in a neural network?

I'm learning about how neural networks are trained. I understand how a neuron works, backpropagation, and all that. In neurons, there is a clear distinction between a "weight" and a "...
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Must all CNNs and RNNs not have a fully connected layer in order to be considered as such?

In the paper Wrist-worn blood pressure tracking in healthy free-living individuals using neural networks, the authors talk about a combination of feed-forward and recurrent layers, as if FC layers ...
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What is the difference between environment states and agent states in terms of Markov property?

I'm going through the David Silver RL course on YouTube. He talks about environment internal state $S^e_t$, and agent internal state $S^a_t$. We know that state $s$ is Markov if $\mathbb{P}\{S_t=s|S_{...
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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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In Probabilistic Graphical Model (written by Daphne Koller), what's the meaning of “parameter” in representation of the distribution?

I just started to read the PGM book written by Daphne Koller. In the chapter of Bayesian Network Representation(Chapter 3), there are some descriptions about the standard parameterization of the joint ...
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28 views

Does a trajectory in reinforcement learning contain the last action?

From what I learn from CS285 and OpenAI's spinning up, a trajectory in RL is a sequence of state-action pairs: $$\tau = \{s_0, a_0, ..., s_t, a_t\}$$ And the resulting trajectory probability is: $$ P(\...
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Why does self-supervised representation learning (such as SimpleSiam) use a ResNet encoder that is trained in a supervised fashion?

Can anybody explain to me why does self-supervised representation learning on images using Siamese neural networks (such as SimpleSiam (https://arxiv.org/abs/2011.10566), SimCLR, Boyl) use a ResNet ...
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What is the name of this algorithm that estimates the gradient with an average by sampling from a distribution?

Consider maximizing the function $R(w)$ with parameter $w$ using gradient ascent. However, we don't know the gradient $\nabla_wR(w)$ formula. Now suppose $w$ is sampled from a probability distribution ...
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Where does the hierarchical reinforcement learning framework name “MAXQ” come from?

I've been researching different frameworks for hierarchical RL (mainly options, HAMs, and MAXQ) and noticed that both options and HAMs have names that relate to how they function. I can't seem to find ...
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Can CNNs be made robust to tricks where small changes cause misclassification?

I while ago I read that you can make subtle changes to an image that will ensure a good CNN will horribly misclassify the image. I believe the changes must exploit details of the CNN that will be used ...
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What is Federated Learning?

How would you explain Federated Learning in simple layman terms for a non-STEM person? What are the main ideas behind Federated Learning?
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Is there any difference between ConvNet and CNN?

ConvNet stands for Convolutional Networks and CNN stands for Convolutional Neural Networks. Is there any difference between both? If yes, then what is it? If no, is there any reason behind using ...
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What is the relation between the context in contextual bandits and the state in reinforcement learning?

Conceptually, in general, how is the context being handled in contextual bandits (CB), compared to states in reinforcement learning (RL)? Specifically, in RL, we can use a function approximator (e.g. ...
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What are the differences between an agent and a model?

In the context of Artificial Intelligence, sometimes people use the word "agent" and sometimes use the word "model" to refer to the output of the whole "AI-process". For ...
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Visualizing the Loss Landscape of Neural Nets: Meaning of the word 'filter'?

I found myself scratching my head when I read the following phrase in the paper Visualizing the Loss Landscape of Neural Nets: To remove this scaling effect, we plot loss functions using filter-wise ...
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Aren't scores in the Wasserstein GAN probabilities?

I am quite new to GAN and I am reading about WGAN vs DCGAN. Relating to the Wasserstein GAN (WGAN), I read here Instead of using a discriminator to classify or predict the probability of generated ...
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What is a “learned policy” in Q-learning?

I am completing an assignment at the moment. One of the assignment questions asks how you identified the learned policy and how you obtained it. The question is a reinforcement learning question, and ...
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What is the smoothness assumption in SVMs?

In this research paper, we have the following claim the smoothness assumption that underlies many kernel methods such as Support Vector Machines (SVMs) does not hold for deep neural networks trained ...
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Is a team of ML scientists an “intelligent agent”?

I am writing about the role of machine learning scientists in developing a solution. Is there a term for the humans who do learning? Can we call a "team of machine learning scientists with their ...
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Is my reward function non-Markovian?

I am working on an RL problem where the time when the agent obtains the reward for taking action $a$ in time step $t$ is stochastic. In fact, there is no immediate reward for taking action $a$ in time ...
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What are support values in a support vector machine?

I started reading up on SVM and very little is defined of what are support values. I reckon it's they are denoted as $\alpha$ in most formulations.
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Confusion between function learned and the underlying distribution

Let us assume that I am working on a dataset of black and white dog images. Each image is of size $28 \times 28$. Now, I can say that I have a sample space $S$ of all possible images. And $p_{data}$ ...
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In the machine learning literature, what does it mean to say that something is “embedded” in some space?

In the machine learning literature, I often see it said that something is "embedded" in some space. For instance, that something is "embedded" in feature space, or that our data ...
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What is a “codon” in grammatical evolution?

The term codon is used in the context of grammatical evolution (GE), sometimes, without being explicitly defined. For example, it is used in this paper, which introduces and describes PonyGE 2, a ...
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What is a unified neural network model?

In many articles (for example, in the YOLO paper, this paper or this one), I see the term "unified" being used. I was wondering what the meaning of "unified" in this case is.
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What is MNLI-(m/mm)?

I came across the term MNLI-(m/mm) in Table 1 of the paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. I know what MNLI stands for, i.e. Multi-Genre Natural ...
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What is asymmetric relaxation backpropagation?

In Chapter 8, section 8.5.2, Raul Rojas describes how the weights for a layer of a neural network can be calculated using a pseudoinverse of the sigmoid function in the nodes, he explains this is an ...
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What's the difference between architectures and backbones?

In the paper "ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery", the authors talk about using: Feature Pyramid Networks (as the ...
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$\frac{P(x_1 \mid y, s = 1) \dots P(x_n \mid y, s = 1) P(y \mid s = 1)}{P(x \mid s = 1)}$ indicates that naive Bayes learners are global learners?

I am currently studying the paper Learning and Evaluating Classifiers under Sample Selection Bias by Bianca Zadrozny. In section 3. Learning under sample selection bias, the author says the following: ...
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Why are “Transformers” called this way?

What is the reason behind the name "Transformers", for Multi Head Self-Attention-based neural networks from Attention is All You Need? I have been googling this question for a long time, and ...
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What is the difference between sensitivity analysis and parameter tuning?

I tried different values of genetic algorithm operators: many crossover rates from 20% to 80% many crossover rates from 1% to 20% varying the population size The study of different parameter values ...
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What is the reason for taking tuples as vectors rather than points?

Across the literature of artificial intelligence, especially machine learning, it is normal to treat the tuples of datasets as vectors. Although there is a convention to treat them as data points. ...
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What do we mean by “infrequent features”?

I am reading this blog post: https://ruder.io/optimizing-gradient-descent/index.html. In the section about AdaGrad, it says: It adapts the learning rate to the parameters, performing smaller updates (...
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What is the formal terminology for emotion recognition AI?

I'm researching the use of emotion recognition in Intelligent Tutoring Systems and trying to more effectively find and formally reference materials. My question is whether this is the most formal ...
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What is meant by “ground truth” in the context AI?

What does "ground truth" mean in the context of AI especially in the context of machine learning? I am a little confused because I have read that the ground truth is the same as a label in ...
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What is the meaning of “exploration” in reinforcement and supervised learning?

While exploration is an integral part of reinforcement learning (RL), it does not pertain to supervised learning (SL) since the latter is already provided with the data set from the start. That said, ...
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What is a “learned emulator”?

In this article, the term "learned emulator" is used. Recently, scientists have started creating "learned emulators" using AI neural network approaches, but have not yet fully ...
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418 views

Are mult-adds and FLOPs equivalent?

I am comparing different CNN architectures for edge implementation. Some papers describing architectures refer to mult-adds, like the MobileNet V1 paper, where it is claimed that this net has 569M ...
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Why is the target called “target” in Monte Carlo and TD learning if it is not the true target?

I was going through Sutton's book and, using sample-based learning for estimating the expectations, we have this formula $$ \text{new estimate} = \text{old estimate} + \alpha(\text{target} - \text{old ...
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What is the difference between active learning and online learning?

The definitions for these two appear to be very similar, and frankly, I've been only using the term "active learning" the past couple of years. What is the actual difference between the two? ...
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What does the notation $\mathcal{N}(z; \mu, \sigma)$ stand for in statistics?

I know that the notation $\mathcal{N}(\mu, \sigma)$ stands for a normal distribution. But I'm reading the book "An Introduction to Variational Autoencoders" and in it, there is this notation:...

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