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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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What is "canonical space" term?

I am reading a paper in 3D reconstruction but encounter the term “canonical space”. I want to ask: What is “canonical space”? Is it widely used? Is there other use of the term? The term is mentioned ...
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Is early stopping a collection of techniques or a single technique?

Early stopping is a regularization technique in neural network training. It avoids overfitting. I have doubts about early stopping that whether it refers to a single technique (sense #1) or a family ...
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What are all the possible usages of 'multilayer perceptron'?

The term 'multilayer perceptron' has been used in literature in various ways in the literature. I am presenting some of them below As a feed-forward neural network [1]. As a fully connected feed-...
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Is the "Helvetica scenario" mentioned here related to Artificial Intelligence?

Consider the following sentence from the original GAN paper titled Generative Adversarial Nets in particular, $G$ must not be trained too much without updating $D$, in order to avoid "the ...
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What is the difference between features and inputs in machine learning?

I have seen many places that features and inputs have been used interchangeably when talking about machine learning especially deep neural networks. I want to know if they are indeed the same thing or ...
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What do "large variables" and "small weights" mean in these sentences?

I'm trying to understand these two points from an article: Models with large variables i.e weight matrices. As a consequence such models have correspondingly large gradients and optimizer states. The ...
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What is a 'degenerate run' in evaluating model performance?

I've recently come across a paper that uses the term "degenerate run", but I'm not sure if I understand what it means. The idea is that when they report the average performance of running ...
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What does "position" in "each position in the decoder" denote in the Transformer's original paper?

I am reading Attention is All You Need and I feel confused about the word "position" in this paper, by the way I'm not native English speaker which may cause my confusion which has confused ...
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How to construct a reward function for a "wait and see" problem

I'm working on a problem that I think could probably be represented as a reinforcement learning task, but I'm uncertain about how to design the reward function. The core task is essentially a ...
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Which algorithm can find the best combination of players to maximize the chance of getting a high score?

I am looking for the right terminology for this problem, so I know what to learn about. Imagine a population of 100 people in a town. The town has a sport team with 10 positions that play in ...
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Does the term "data augmentation" imply increasing the training dataset?

I have a manuscript that has been reviewed and one of the reviewers commented on my use of the term " data augmentation", saying that it might not be the appropriate term in my case (...
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Should I need to interpret the word "metric" in "performance metric" rigorously?

Consider the following abstract from the research paper titled A Note on the Inception Score for instance Deep generative models are powerful tools that have produced impressive results in recent ...
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What is the name of a feature space which has consistant distance-related properties?

What is the word describing a feature space where distance between two elements has a decisive informational value, whatever the pair of elements is? For example if a model creates embeddings for ...
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What does it mean by "gradient flow" in the context of neural networks?

Several research papers and textbooks (e.g. this) contain the phrase "gradient flow" in the context of neural networks. I am confused about whether it has any rigorous and formal way of ...
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What does it mean by "dynamics of a sequence" mathematically?

Consider the following paragraph from the topic named sequential models from the textbook titled Dive into Deep Learning Both cases raise the obvious question of how to generate training data. One ...
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Why is it called "area of union" when calculating the Intersection over Union?

When calculating the Intersection Over Union the following explanation is widely used. (Source: A Survey on Performance Metrics for Object-Detection Algorithms, by Padilla et al. 2020) The image and ...
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Is there any terminology distinguishing out-of-sample-distribution vs. out-of-population-distribution?

Out-of-distribution (OOD) datasets are getting attention recently. I understand that OOD is defined with respect to (wrt) the training set. A training set is a sample of the population of interest. ...
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Is there any subtle difference between kernel and filter in the context of neural netowrks?

Consider the following excerpt from a paragraph, taken from the topic Detecting features with convolutions of the textbook named Deep Learning with PyTorch by Eli Stevens et al., regarding ...
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Is there a standard term for the following flaw in the data?

I wonder if following characteristic of data has some standard "professional" or scientific term associated with it. Let's assume that I have a set of dog/cat images labeled 0 for a cat and ...
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What do state features mean in the context of inverse RL?

I am reading Zeibart's Inverse RL paper, and it states - The agent is assumed to be attempting to optimize some function that linearly maps the features of each state, $f_{sj} \in \mathbb{R}^k$, to a ...
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What does it mean by "lazy mean" here?

Consider the following paragraph, taken from 3.4: Named Tensors of the textbook named Deep Learning with PyTorch by Eli Stevens et al., regarding the calculation of the mean for RGB channels of an RGB ...
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What is meant by "lateral connection" in the context of neural networks?

A class of CNN is popular due to the implementation of residual connections. We can use both terms "residual connections" and "skip connections" interchangeably as they refer to ...
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Is the phrase "Feature Pyramid Network" refer to CNN only?

"Feature Pyramid Network" is a network that is used for feature extraction. Since it is pyramid in shape, it might be called so. Consider the following excerpts from two different sources #1 ...
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Is there any subtle difference between recursive computation and recurrence computation?

Consider the following excerpt paragraph taken from the section titled "Unfolding Computational Graphs" of the chapter 10: Sequence Modeling: Recurrent and Recursive Nets of the textbook ...
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What does "statistical strength" mean in this context?

Consider the following excerpt from a paragraph taken from chapter 10: Sequence Modeling: Recurrent and Recursive Nets of the textbook named Deep Learning by Ian Goodfellow et al regarding the ...
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Is a recurrent layer same as LSTM or single-layered LSTM?

In MLP, there are neurons that form a layer. Each hidden layer gives a vector of number that is the output of that layer. In CNN, there are kernels that form a convolutional layer. Each layer gives ...
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What does "Gau" in GauGAN stand for?

GauGAN is a neural network architecture from NVIDIA that can create realistic images from semantic maps (and nowadays also textual descriptions).
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What is a "mask" in the context o RNN-based encoders?

While reading source code related to RNN encoders, I've come across the term mask as input to the encoder. What exactly is it?
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What does "at inference time" on Tesla's cars mean?

I've watched Tesla AI Day 2021 and there was a question Tesla staff tried to answer, but I did not quite understand the question (Note: quote taken from autogenerated subtitles, I do not hear ...
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What is the stride information of an image referring here?

In convolutional neural networks, the convolution and pooling operations have a parameter known as stride, which decides the amount of jump the kernel needs to do on the input image. You can get more ...
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What does it mean to apply decomposition at inference-time in a machine translation system?

I'm reading this paper for sub-character decomposition for logographic languages and the authors mention decomposition at inference-time. They're using Transformer architecture. More specifically, the ...
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What is the "temperature" in the GPT models?

What does the temperature parameter mean when talking about the GPT models? I know that a higher temperature value means more randomness, but I want to know how randomness is introduced. Does ...
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Is there any difference between vector-valued function and vector field at least for the usage in AI?

I encountered the following statement from the chapter named Vector Calculus from the test book titled Mathematics for Machine Learning by Marc Peter Deisenroth et al. In the following, we will ...
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How is the VAE related to the Autoencoding Variational Bayes (AEVB) algorithm?

I am familiar with the variational autoencoder, but not totally clear on what exactly the AEVB is. In the original VAE paper (by Kingma and Welling), he uses both the terms variational autoencoder and ...
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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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When can we call a feature "hierarchical"?

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 there a widely accepted definition of the width of a neural network?

The depth of a neural network is equal to the total number of layers in the neural network (excluding the input layer by convention). A neural network with "many layers" is called a deep ...
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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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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 as intermediate ...
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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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7 votes
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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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1 vote
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Which of the following probability distribution is generating an iid dataset?

Let $X_1, X_2$ be two discrete random variables. Each random variable takes two values: $1, 2$ The probability distribution $p_1$ over $X_1, X_2$ is given by $$p_1(X_1=1, X_2 = 1) = \dfrac{1}{4}$$ $$...
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Can I always use "encoding" and "embedding" interchangeably?

This question is restricted to the text domain only. The meaning of the word "encode" is Convert (information or instruction) into a particular form. One which performs encoding is called an ...
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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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