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Questions tagged [terminology]

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

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9 views

To denote a training example should I use row vector or column vector?

This code accesses the first 3 examples in the iris data set, from sklearn.datasets import load_iris iris = load_iris() print(iris.data[:3]) and gives ...
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14 views

Product of probabilities raised to own powers that can be used for entropy calculation

Suppose $X$ is a random variable taking $k$ values. $$Val(X) = \{x_1, x_2, x_3, \cdots, x_k\} $$ Then what is the following expression of $N(X)$ called in literature? What does it signify? $$ N(X) = \...
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1answer
39 views

What is the difference between the definition of “accuracy” in machine learning and federated learning?

What is the difference between the definition of "accuracy" in machine learning and federated learning? In particular, how is the accuracy calculated in the following paper: eq (3.12) https:...
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22 views

Why Word2Vec is called a neural model if no neural network is used in it?

Word2Vec model does not use any neural network. It uses logistic regression only. Consider the following paragraph from p:18 of Vector Semantics and Embeddings We’ll see how to do neural ...
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1answer
31 views

What is the meaning of “continuous” in a continuous bag-of-words model?

The word continuous in mathematics is a property of either a set or a function that says that the underlying object has no discontinuity in the range mentioned. If the object is a set, then $[-1,1]$ ...
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2answers
34 views

What is the exact difference between distributional semantics and distributed semantics?

While studying word embeddings in natural language processing, I encountered the following statement on page 327 of the textbook Natural Language Processing by Jacob Eisenstein Distributional ...
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1answer
39 views

What is the difference between terminal state, nonterminal states and normal states?

In Sutton & Barto's Reinforcement Learning: An Introduction, page 54, the authors define the terminal state as following: Each episode ends in a special state called the terminal state But the ...
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11 views

What's mutual exclusivity in meta-learning?

What do we mean by mutual exclusivity of tasks? This work (E Pan, 21) and this one (M Yin, 20) state that most classification meta-learning algorithms fail for non-mutually exclusive tasks as the ...
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2answers
40 views

What is the difference between “ground truth” and “ground-truth labels”?

I'm aware that the ground-truth of the example at the top left-hand corner of the image below is "zero" However, I am confused about the meaning of the terms ground truth and ground-truth ...
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1answer
33 views

Is categorical encoding a type of word embedding?

Word embedding refers to the techniques in which a word is represented by a vector. There are also integer encoding and one-hot encoding, which I will collectively call categorical encoding. I can see ...
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24 views

Do literature uses (text) encoding and (word) embedding in a loose sense?

Many research papers use the word "encoding" in many ways. One way is that they use the word "encoding" for models that do convert the text into a vector (say text encoding). But, &...
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18 views

What is meant by Hinton when he refers to “Part-Whole Hierarchies” in his GLOM framework

I was recently reading Hinton's GLOM idea How to represent part-whole hierarchies in a neural network, and I am simply unsure about what exactly he means when he says parsing images into "part-...
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2answers
277 views

Is an embedding a representation of a word or its meaning?

What does the term "embedding" actually mean? An embedding is a vector, but is that vector a representation of a word or its meaning? Literature loosely uses the word for both purposes. ...
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1answer
35 views

Where do the feature extraction and representation learning differ?

Feature selection is a process of selecting a subset of features that contribute the most. Feature extraction allows to get new features that are not actually present in the given set of features. ...
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14 views

Terminology for the weight of likelihood ratio/score function?

If we estimate the gradient of $f(x)$ using the likelihood ratio/score function, i.e. $$\nabla f = f^*\dfrac{\partial \log p(x)}{\partial \theta}$$ is there any agreed upon terminology to call "$...
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1answer
44 views

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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1answer
50 views

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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17 views

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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30 views

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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1answer
22 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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1answer
219 views

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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2answers
87 views

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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0answers
37 views

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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18 views

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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1answer
93 views

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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1answer
48 views

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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1answer
54 views

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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1answer
67 views

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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0answers
20 views

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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1answer
29 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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8 views

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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0answers
26 views

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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30 views

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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2answers
387 views

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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1answer
62 views

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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1answer
286 views

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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2answers
182 views

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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3answers
1k views

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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0answers
48 views

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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1answer
78 views

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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1answer
179 views

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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1answer
46 views

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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1answer
43 views

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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0answers
57 views

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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1answer
89 views

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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1answer
132 views

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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1answer
81 views

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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1answer
63 views

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 ...
4
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1answer
70 views

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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160 views

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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