Questions tagged [terminology]

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

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104 views

Does regularization just mean using an augmented loss function?

We need to use a loss function for training the neural networks. In general, the loss function depends only on the desired output $y$ and actual output $\hat{y}$ and is represented as $L(y, \hat{y})$. ...
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14 views

Is there any difference between “image generation” and “image synthesis”?

Generative Adversarial networks (aka GANs) are used for image generation. The phrase image synthesis is also used in literature. I know that the phrase image generation stands for An act of ...
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16 views

Is my understanding about the number of iterations correct?

Per google machine-learning glossary, when I have 100 training examples and update my model for each training example, if I train my model 5 epochs without early-stop, there are 500 iterations in ...
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1answer
23 views

What does 'input planes' mean in the phrase 'input signal/image composed of several input planes'?

PyTorch documentation provided the following descriptions to the Convolution layers ...
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30 views

What does 'channel' mean in the case of an 1D convolution?

While reading about 1D-convolution in PyTorch, I encountered the concept of channels ...
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1answer
35 views

What exactly is an XPU?

I know about CPU, GPU and TPU. But, it is the first time for me to read about XPU from PyTorch documentation about MODULE. ...
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10 views

What is language-conditioned visual reasoning?

Can anyone explain what language-conditioned visual reasoning is? I saw this term in this paper and I searched on the internet but I couldn't find a proper explanation.
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30 views

What are the input and output gradients in PyTorch?

Suppose I want to train a neural network with $m-$length inputs of form $x = [x_1, x_2, x_3, \cdots, x_m]$ and $n-$length outputs of form $y = [y_1, y_2, y_3, \cdots, y_n]$. Let the number of ...
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77 views

Is there any difference between affine transformation and linear transformation?

Consider the following statements from A Simple Custom Module of PyTorch's documentation To get started, let’s look at a simpler, custom version of PyTorch’s Linear module. This module applies an ...
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23 views

Is label-embedding similar to one-hot encoding?

In one-hot encoding, a vector is given to each class label. For each class, only one entry of the vector is equal to 1 and the remaining entries are zeros in this encoding. Thus, in one-hot encoding, ...
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1answer
31 views

What does “differentiable architecture” mean?

I'm currently reading a paper that uses CNN's as a base approach to solving some image classification issues and I've found that they kept mentioning the term "Differentiable Architecture", ...
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31 views

Do the terms multi-task and multi-output refer to the same thing in the context of deep learning?

Do the terms multi-task and multi-output refer to the same thing in the context of deep learning (with neural networks)? For example, do neural networks for multi-task learning use multiple outputs? ...
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11 views

Simple example for side information

Many research papers contain the phrase "side information". After a search on side information, I got the following definition from here. In many problems of machine learning and computer ...
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33 views

What is meant by “Zero-Shot Visual Recognition”?

Many recent research papers contain the phrase "Zero-Shot Visual Recognition". What exactly is meant by zero-shot visual recognition? Does the task need only images or also the other data ...
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24 views

Does this code mean the model trains 10 epochs?

Here is an implementation for Perceptron ...
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1answer
49 views

Why is tanh a “smoothly” differentiable function?

The sigmoid, tanh, and ReLU are popular and useful activation functions in the literature. The following excerpt taken from p4 of Neural Networks and Neural Language Models says that ...
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21 views

What is the difference between model setup, model configuration, and model customization?

In the context of research papers related to deep learning models, the authors usually mention these terms in the experiment section when they are talking about the model: configuration, setup. For ...
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1answer
67 views

What does it mean by overfitting the test set?

Consider the following statement from p14 of Naive Bayes and Sentiment Classification While the use of a devset avoids overfitting the test set, having a fixed training set, devset, and test set ...
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1answer
27 views

Are the held-out datasets used for testing, validation or both?

I came across a new term "held-out corpora" and I confused regarding its usage in the NLP domain Consider the following three paragraphs from N-gram Language Models #1: held-out corpora as a ...
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2answers
69 views

Is there any model that is probabilistic but not statistical?

While studying about the n-gram models, I encountered the terms "statistical model" and "probabilistic model" several times. I got a basic doubt that will there be any ...
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1answer
58 views

Which policy do I need to use in updating Q function?

Policy function can be of two types: deterministic policy and stochastic policy. Deterministic policy is of the form $\pi : S \rightarrow A$ Stochastic policy is defined using conditional probability ...
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1answer
21 views

What do the terms 'Bellman backup' and 'Bellman error' mean?

Some RL literature use terms such as: 'Bellman backup' and 'Bellman error'. What do these terms refer to?
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1answer
28 views

Which tasks are called as downstream tasks?

The following paragraph from p331 of the textbook Natural Language Processing by Jacob Eisenstein. It mentions about certain type of tasks called as downstream tasks. But, it provide no further ...
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1answer
53 views

What is the meaning or implications of the rank of a dataset for machine learning algorithms?

Consider a dataset with $n$ training examples and $d$ features. Let $D_{n \times d}$ be the data matrix and $r$ be the rank of it. In matrices, rank $r$ is generally useful in Knowing the dimension ...
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16 views

Is the compression function for SHA hash algorithms a hidden layer in a neural net?

Is the compression function for SHA-256 and SHA-512 a "hidden layer" in a neural net? If so, what type of neural net is it in? SHA-256 and SHA-512 compression function: source: NIST, “...
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3answers
54 views

Is there a recent book that covers the theoretical and philosophical aspects of artificial intelligence?

What are some recent books that introduce AI and neural networks while also discussing the related philosophical issues, like epistemology and whether AI is really thinking, etc.?
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30 views

What do “spatial” and “temporal” mean in the context of image processing?

I am new to image processing. I am trying to understand CNNs from this blog post. Here's an excerpt from that article that mentions these terms. A ConvNet is able to successfully capture the Spatial ...
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1answer
36 views

What are the 'noisy factors' leading to overfitting?

Consider the following excerpt from section 5.5 Regularization (p. 13) of this chapter Logistic Regression. There is a problem with learning weights that make the model perfectly match the training ...
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16 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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25 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
61 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: Cai, Lingshuang,...
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45 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
39 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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40 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
56 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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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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55 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
43 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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32 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 or ...
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21 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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293 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
37 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
52 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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21 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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34 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
25 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
225 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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94 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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