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 beam?
For example, faster-whisper's transcribe function takes an argument
beam_size: Beam size to use for decoding.
What does "...
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What is curriculum learning in reinforcement learning?
I recently came across the term "curriculum learning" in the context of DRL and was intrigued by its potential to improve the learning process. As such, what is curriculum learning? And how ...
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Is manual binding output to input also an AI?
I know AI is primarly training a machine by samples of input-output in order it would learn itself about relations between the input and the output.
What if I manually add the relations? Is that still ...
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Term for algorithms that are not trained
Before the advent of neural architectures, many AI domains (e.g. speech recognition and computer vision) used algorithms that consisted of a series of hand-crafted transformations for feature ...
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Are "prompt engineering" and "prompt design" used as synonymous?
Are "prompt engineering" and "prompt design" used as synonymous / equivalent terms on the day to day communications (not research papers) in Artificial Intelligence community ? Do ...
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When is it necessary to explicitly define both the state and observation space in a custom environment?
I'm fairly new to reinforcement learning concepts, and I'm trying to implement a simple custom environment. In my custom environment, I have a scenario where I have multiple continuous state spaces, ...
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What is the difference between the term "generative" in classical machine learning and deep learning?
There are lots of explanations on DGM (Deep Generative Model) and generative classifier (most of the explanations on which are about generative classifier vs discriminative classifier)
But, I can ...
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Do the terms 'sample complexity' and 'sample efficiency' mean the same thing in RL context
For example, the the paper Soft Actor-Critic:Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor, both terms are mentioned but without explaining. I have seen them in other ...
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Sutton & Barto: what are parametrized functions?
From "Reinforcement Learning: An introduction (2nd ed.)" by Richard S. Sutton and Andrew G. Barto, on page 59
Instead, the agent would have to maintain $v_\pi$ and $q_\pi$ as parameterized ...
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What does "relaxation" mean in machine learning context?
I have encountered the term "relaxation" in several papers in the field of machine learning. It seems that most of the authors based on their prior knowledge have used this term for ...
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Is my understanding correct regarding the difference between policy and plan?
I am confused regarding the difference between policy and plan in reinforcement learning. According to my understanding, when we calculate the value of state using Bellman equation in deterministic ...
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Does this property in product fuzzy logic have a name and any consequences?
In product fuzzy logic, the $AND$ operator of two variables $x_0$ and $x_1$ is the product $x_0x_1$.
Using the $NOT(x)$ as $1-x$, expressions for the other three minterms are easily obtained.
$$\...
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Are there use cases of this "leaky hard sigmoid" activation function?
While trying to organize activation functions in a grid, this structure came up:
softplus -> relu -> leaky relu
sigmoid -> hard sigmoid -> unknown?
A ...
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What is the definition of a continuous state/action space?
This question is a result of a discussion with one of my more math-minded friends. When I accidentally mentioned the term continuous state space, he corrected me by saying that I am most probably ...
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Why could there be "information leak" if we do not use fixed horizons?
In this page Limitations on horizon length from the Imitation library, the authors recommend that the user sticks to fixed horizon experiments because there could be "information leak" ...
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What approach finds an aproximation to a function provided only score?
I want to approximate an expensive function without having the training data of correct input-output pairs, instead having the learning model quarry specific input-output pairs and my supervisor (if ...
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Confusion about bias in McCulloch-Pitts neurons
I just have a quick question, maybe I am too nit picky here.
We recently had an introductory lecture to AI in university and the professor talked about McCulloch-Pitts neurons, e.g. activation as soon ...
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Exact definition of WRN-d-k (Wide ResNet)
I am a little confused about the WRN-d-k notation from Wide Residual Networks. To quote the paper,
In the rest of the paper we use the following notation: WRN-n-k denotes
a residual network that has ...
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What is the relationship between data science, artificial intelligence,machine learning and computer vision?
I am beginner to this field and i am trying to find big picture and i have tried to explore youtube and google images in this regard. According to my understanding ,machine learning is subset of ...
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What is the difference between representation and embedding?
As I searched about this two terms, I found they are somehow like each other, both try to create a vector from raw data as I understood. But, what is the difference of this two term?
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What should be taken as random variables in the distributions of datasets?
Consider the following two paragraphs taken from the paper titles Generative Adversarial Nets by Ian J. Goodfellow et.al
#1: Abstract
We propose a new framework for estimating generative models via ...
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What is single object localization?
Object detection is said to be combination of object localization and image classification. However, when reviewing localization, I often come across the term "single-object" localization, ...
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Where does the "rectified" in ReLU come from?
ReLU stands for Rectified Linear Unit. Linear Unit, I understand, since the function is piecewise linear. But what does rectified mean?
I looked up the definition and it said:
denoting an electric ...
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Is there a name for this model?
I have an image autoencoder model trained as follows:
Step 1) train a GAN to obtain a generator capable of drawing from the data manifold by sampling a normal distribution in latent space
Step 2) ...
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Is item-based collaborative filtering the same thing as content-based filtering?
According to this Google dev page
content-based filtering
Uses similarity between items to recommend items similar to what the
user likes.
collaborative filtering
Uses similarities between queries ...
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129
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What exactly is the AI explainability problem?
I am pretty new to AI and have recently been paying attention to AI explainability and the fact that it remains a hurdle within the path of commercializing certain AI systems in health for instance. I ...
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What are semantic word spaces in NLP?
In the abstract of this paper, it's written
Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way.
I would like to understand what semantic ...
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What is a "canonical space"?
I am reading the paper on 3D reconstruction, ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction, and I encountered the term "canonical space".
What is a "...
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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 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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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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