Questions tagged [terminology]

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

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What is a filter in the context of graph convolutional networks?

In Section 2.1 of the research paper titled Semi-Supervised Classification with Graph Convolutional Networks by Thomas N. Kipf et al., Spectral convolution on graphs defined as The multiplication of ...
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What does "unknown search spaces" mean in the context of Evolutionary Algorithms?

In the article Multi-Verse Optimizer: a nature-inspired algorithm for global optimization (DOI 10.1007/s00521-015-1870-7), it's written The results of the real case studies also demonstrate the ...
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What do we mean by "orderly opinions" in this sentence in the context of Bayes theorem?

In this page, it's written (emphasis mine) If probabilities are thought to describe orderly opinions, Bayes theorem describes how the opinions should be updated in the light of new information What ...
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24 views

When can we call a feature "hierarchial"?

Features in machine learning are the attributes of the elements of a data set. They are considered as random variables in probability. Consider the following excerpt from 1.1: The deep learning ...
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What is meant by "spatial encoding" in the context of convolutional neural networks?

Consider the following excerpt from the abstract of the research paper titled Squeeze-and-Excitation networks by Jie Hu et al. Convolutional neural networks are built upon the convolution operation, ...
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Is "width of a neural network" a wrong phrase?

Depth of the neural network is equal to the total number of layers in the neural network except input layer. so, neural network with more number of layers are called deep neural networks. Width, in ...
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Why actual mapping is called as unreferenced mapping in this context of residual framework?

Consider the following statements from the research paper titled Deep Residual Learning for Image Recognition by Kaiming He et al. #1: We explicitly reformulate the layers as learning residual ...
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Do authors generally use fully connected layer instead of affine transformation?

We generally encounter the following statement several times The input vector is first fed into a fully connected layer...... Since linear activation functions, such as identity function, can so ...
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24 views

What does it mean by "low-level" and "high-level" in features generated by CNN?

Across the literature, the terms "high-level" and "low-level" are generally used as an adjective to the features generated by the convolution neural network. Should I understand ...
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What are the different possible usages of the word "i.i.d" in machine learning?

The acronym "iid" stands for "independent and identically distributed". It is a property of a sequence of random variables. You can read here for more details. This question is ...
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Is LSTM a subcategory of RNN?

Is the LSTM-Architecture a subcategory of RNNs? Or are they totally different? Literature doesn't seem to be unitary on this. This figure appears to explain the models to be alternatives, but I ...
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Can I always use "encoding" and "embedding" interchangeably?

This question is restricted to text domain only. The meaning of word "encode" is Convert (information or an instruction) into a particular form. One which performs encoding is called encoder....
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What is meant by domain in the notations of geometric deep learning?

While reading the Notation of the paper titled Geometric Deep Learning Grids, Groups, Graphs, Geodesics, and Gauges, I came across the following notations. $$ \...
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Is the inductive bias always a useful bias for generalisation?

Is it true that a bias is said to be inductive iff it is useful in generalising the data? Or does inductive bias can also refer to the assumptions that may cause a decrease in performance? Suppose I ...
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What does it mean by bottleneck and representational bottleneck in feedforward neural networks?

Consider the following paragraph from section 2: General Design Principles of the research paper titled Rethinking the Inception Architecture for Computer Vision ...
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What is meant by a "relevant document" in NLP?

In natural language processing, I came across the concept of "relevant document" several times. And several analytical formulas, such as precision, recall are based on the relevant documents....
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Is there any difference between the phrases "text representation" and "text feature representation"?

Text representation, in simple words, is representing text in sensible numeric form. You can read in detail from the following paragraph Text representation is one of the fundamental problems in text ...
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What is the fundamental difference between the synthesis task and sampling task?

Among the list of tasks in machine learning, synthesis and sampling is one of the key task. Consider the following explanation regarding synthesis and sampling task from Chapter 5: Machine Learning ...
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Why does critical points and stationary points are used interchangeably?

Consider the following paragraph from Numerical Computation of the deep learning book. When $f'(x) = 0$, the derivative provides no information about which direction to move. Points where $f'(x)$ = 0 ...
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Where can I read about the multinoulli distribution?

I encountered the term multinoulli distribution in the following sentence from Chapter 4: Numerical Computation of the deep learning book. The softmax function is often used to predict the ...
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24 views

Why not undefined expression is different from numerical underflow?

Consider an architecture or programming language that uses $n$ bits for storing a floating point number in a particular format. Then each and every floating point number it can store should be in a ...
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34 views

What is the difference between “AI Methods” and “AI Techniques”?

These are words that we frequently come upon. What can be said about the differences? Would these two words' subheadings be different?
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What exactly is a grid-like topology according to the book Deep Learning?

I am reading this book called "Deep Learning" (by Goodfellow, Bengio and Courville). On page 326, in the first paragraph, it says: CNNs, are a specialized kind of neural network for ...
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Assume 120 examples, a model makes 20 correct predictions and updates weight for the other 100. Should I count this epoch 100 iterations or 120?

Per google's glossary, an iteration refers to A single update of a model's weights during training ... The following code comes from a github repo ...
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Is there any difference between an objective function and a value function?

I found the usage of both objective function and value function in the same context. Context #1: In the paper titled Generative Adversarial Nets We simultaneously train G to minimize $\log(1 −D(G(z)))...
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What is numerical stability?

I came across the phrase "numerical stability" several times. But almost in the same context. I encountered this word mostly in the analytical formula for batch normalization. $$y = \dfrac{...
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45 views

What does it mean by strong or sufficient gradient for training in this context?

It has been mentioned in the research paper titled Generative Adversarial Nets that generator need to maximize the function $\log D(G(z))$ instead of minimizing $\log(1 −D(G(z)))$ since the former ...
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Is "kernel" different from "filter" in convolutional neural networks?

Recently I asked a question on how a convolution 2d layer changes an RGB image into a grayscale image. Assume that our task is to convert an RGB image into a grayscale image. I use to believe that ...
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348 views

What exactly is a Parzen?

I came across the term "Parzen" while reading the research paper titled Generative Adversarial Nets. It has been used in the research paper in two contexts. #1: In phrase "Parzen window&...
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Why is it called "batch" gradient descent if it consumes the full dataset before calculating the gradient?

While training a neural network, we can follow three methods: batch gradient descent, mini-batch gradient descent and stochastic gradient descent. For this question, assume that your dataset has $n$ ...
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Is there a term for performance metric like prediction time on a new/unseen example?

The performance entry on Google's machine-learning glossary doesn't mention prediction time on a new/unseen example which is important for production use. Is there a term to refer to that metric?
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What does "linear unit" mean in the names of activation functions?

Activation functions, in neural networks, are used to introduce non-linearity. Many activation functions that are used in neural networks have the term "Linear Unit" in their full form. &...
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Is my understanding on "smooth approximation" correct?

Consider the following details regarding Softplus activation function $$\text{Softplus}(x) = \dfrac{\log(1+e^{\beta x})}{\beta}$$ SoftPlus is a smooth approximation to the ReLU function and can be ...
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What is meant by an axis of a tensor?

Tensor is an ordered collection of elements. The elements are generally real numbers. Tensors are used in deep learning for storing data. There is a wide usage of the word "axis" related to ...
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Does "fusion" in "feature fusion" has any formal definition?

I encountered the phrase "fusing features" several times in the literature. I am providing an excerpt from a research paper to provide context for usage of the word fusion. The reason is ...
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Why is the activation function "HardShrink" called so?

Neural networks contain activation functions, which are responsible for the non-linearity of their intermediate and final outputs. Hardshrink is the name of an activation function, which is defined ...
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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