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 the Bellman operator in reinforcement learning?

In mathematics, the word operator can refer to several distinct but related concepts. An operator can be defined as a function between two vector spaces, it can be defined as a function where the ...
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22 views

What is an activation of a unit $k$ in the $l$-th layer?

I have been reading research papers on GAIN. In the paper Tell Me Where to Look: Guided Attention Inference Network (section 3.1), the author says: let $f_{l},_k$ be the activation of unit $k$ in the ...
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7answers
512 views

Is transistor the first artificial intelligence?

Artificial Intelligence is any device that perceives its environment and takes actions that maximize its chance of success at some goal. I got this definition from Wikipedia that cited "Russell and ...
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1answer
26 views

What is the representational capacity of a learning algorithm?

The definition I see for representational capacity is "the family of functions the learning algorithm can choose from when varying the parameters in order to reduce a training objective." (...
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3answers
1k views

Do GANs come under supervised learning or unsupervised learning?

Do GANs come under supervised learning or unsupervised learning? My guess is that they come under supervised learning, as we have labeled dataset of images, but I am not sure as there might be other ...
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1answer
62 views

Who first coined the term “deep learning”?

AFAIK, deep learning became popular in 2012 with the victory of ImageNet Competition - Large Scale Visual Recognition Challenge 2012 where winners of this contest actually used deep learning ...
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71 views

What is Cognitive Intelligence?

Similarly to the question, What is artificial intelligence? Cognitive Intelligence, as well as being a part of Artificial Intelligence, is an area that mainly covers the technology and tools that ...
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7answers
2k views

What is artificial intelligence?

What is the definition of artificial intelligence?
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43 views

Does Algorithmic Mechanism Design come under the field of AI?

I see many papers in AAMAS talk about artificial intelligence and mechanism design simultaneously. I was wondering, for the sake of being pedantic, is mechanism design could be classified under AI.
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What is meant by “arranging the final features of CNN in a grid” and how to do it?

In the paper What You Get Is What You See: A Visual Markup Decompiler, the authors have proposed a method to extract the features from the CNN and then arrange those extracted features in a grid to ...
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What is an activity vector in capsule neural networks?

I was reading the paper Dynamic Routing Between Capsules and didn't understand the term "activity vector" in the abstract. A capsule is a group of neurons whose activity vector represents the ...
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3answers
367 views

What is meant by “known” in “A knowledge-base exhibits complete knowledge if and only if, for every $P$, $P$ or $\neg P$ is known”?

I have a question as to what it means for a knowledge-base to be consistent and complete. I've been looking into non-monotonic logic and different formalisms for it from the book "Knowledge ...
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1answer
50 views

What is meant by degrees of freedom of latent variables?

...Designing such a likelihood function is typically challenging; however, we observe that features like spectrogram are effective when latent variables have limited degrees of freedom. This motivates ...
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287 views

How Swarm Intelligence can empower Blockchain?

Are there examples of applications in blockchain consensus using swarm intelligence, as opposed to classical consensus mechanisms like PoW or PBFT? Please note that recent classical consensuses, ...
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1answer
51 views

How can I classify policy gradient methods in RL?

In the book of Barto and Sutton, there are 3 methods presented that solve an RL problem: DP, Monte Carlo, and TD. But which category does policy gradient methods (or actor-only methods) classify in? ...
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49 views

What are the differences between backbones, frontends, models and architectures in applied deep learning?

Context I'm trying to dive into deep learning for tasks on images, and trying to figure out how to reuse some well-known structures* that have been published, mainly on github. ( *Here, structure can ...
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77 views

What is a self-learning model?

Any simple example of a self-learning model (any business use case, banking)? I have found the terms here and here.
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1answer
45 views

What does it mean when a model “statistically outperforms” another?

I was reading this paper where they are stating the following: We also use the T-Test to test the significance of GMAN in 1 hour ahead prediction compared to Graph WaveNet. The p-value is less than 0....
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289 views

Is reinforcement learning using shallow neural networks still deep reinforcement learning?

Often times I see the term deep reinforcement learning to refer to RL algorithms that use neural networks, regardless of whether or not the networks are deep. For example, PPO is often considered a ...
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2answers
128 views

What are the main algorithms used in computer vision?

Nowadays, CV has really achieved great performance in many different areas. However, it is not clear what a CV algorithm is. What are some examples of CV algorithms that are commonly used nowadays and ...
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3answers
871 views

What is Reinforcement Learning?

What is the cleanest, easiest way to explain someone who is a non-STEM work colleague the concept of Reinforcement Learning? What are the main ideas behind Reinforcement Learning?
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1answer
982 views

What is the difference between pixel-based object recognition and feature-based object recognition?

From my understanding and text I found in research papers online : Pixel-based object recognition: neural networks are trained to locate individual objects based directly on pixel data. Feature-based ...
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1answer
74 views

Why do we use the word “kernel” in the expression “Gaussian kernel”?

I've heard the expression "Gaussian kernel" in several contexts (e.g. in the kernel trick used in SVM). A Gaussian kernel usually refers to a Gaussian function (that is, a function similar to the ...
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1answer
238 views

What do the words “coarse” and “fine” mean in the context of computer vision?

I was reading the well know paper Fully Convolutional Networks for Semantic Segmentation, and, throughout the whole paper, they talk use the term fine and coarse. I was wondering what they mean. The ...
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281 views

Is the word “pose” used correctly in the paper “Matrix Capsules with EM Routing”?

In traditional computer vision and computer graphics, the pose matrix is a $4 \times 4$ matrix of the form $$ \begin{bmatrix} r_{11} & r_{12} & r_{12} & t_{1} \\ r_{21} & r_{...
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1answer
181 views

How do I recognise a bandit problem?

I'm having difficulty understanding the distinction between a bandit problem and a non-bandit problem. An example of the bandit problem is an agent playing $n$ slot machines with the goal of ...
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216 views

What is the name of a human-inspired machine learning approach?

I once came across a neural network being trained without back-propagation or genetic algorithms (or using any kind of data sets). It was based on how the human brain learns and adjusts its ...
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What is the definition of each of these neural network cell types?

I found this nice-ish-looking diagram, but it has a wholly inadequate descriptions for each of the cell types, aside from including names. What is the definition/description of each of these cell ...
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1answer
33 views

What is the meaning of “easy negatives” in the context of machine learning?

What does the term "easy negatives" exactly mean in the context of machine learning for a classification problem or any problem in general? From a quick google search, I think it means just negative ...
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2answers
59 views

What is the difference between artificial intelligence and swarm intelligence?

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine ...
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1answer
37 views

What are finite horizon look-ahead policies in reinforcement learning?

I was reading the paper How to Combine Tree-Search Methods in Reinforcement Learning published in AAAI Conference 2019. It starts with the sentence Finite-horizon lookahead policies are abundantly ...
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6answers
2k views

What is fuzzy logic?

I'm new to A.I. and I'd like to know in simple words, what is the fuzzy logic concept? How does it help, and when is it used?
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50 views

What are the exact meaning of “lower-order structure” and “higher-order structure” in this paper?

I recently read a paper on community detection in networks. In the paper EdMot: An Edge Enhancement Approach for Motif-aware Community Detection, the authors consider the "lower-order structure" of ...
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2answers
580 views

In what ways is the term “topology” applied to Artificial Intelligence?

I have a only a general understanding of General Topology, and want to understand the scope of the term "topology" in relation to the field of Artificial Intelligence. In what ways are topological ...
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1answer
37 views

What are mono-variable and multi-variable neural networks?

In this document, the terms "Redes Neuronales estáticas monovariables" and "Redes Neuronales estáticas multivariables" are mentioned. What are mono-variable and multi-variable neural networks? Is it ...
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6answers
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What is the purpose of an activation function in neural networks?

It is said that activation functions in neural networks help introduce non-linearity. What does this mean? What does non-linearity mean in this context? How does the introduction of this non-...
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17 views

How do CNNs or RNNs “stack the feature of nodes by a specific order”?

I am trying to understand the following statement taken from the paper Graph Neural Networks: A Review of Methods and Applications (2019). Standard neural networks like CNNs and RNNs cannot handle ...
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1answer
80 views

What is local consistency in constraint satisfaction problems?

In the Constraint Propagation in CSP, it is often stated that pre-processing can solve the whole problem, so no search is required at all. And the key idea is local consistency. What does this ...
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3answers
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What is the difference between actor-critic and advantage actor-critic?

I'm struggling to understand the difference between actor-critic and advantage actor-critic. At least, I know they are different from asynchronous advantage actor-critic (A3C), as A3C adds an ...
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1answer
669 views

Are iterative deepening, principal variation search or quiescence search extensions of alpha-beta pruning?

I know that there are several optimizations for alpha-beta pruning. For example, I have come across iterative deepening, principal variation search, or quiescence search. However, I am a little bit ...
3
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1answer
125 views

What does “off-the-shelf” mean?

I encountered the phrase/concept off-the-shelf CNN in this paper in which authors used off-the-shelf CNN representation, OverFeat, with simple classifiers to ...
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1answer
24 views

What are non-held-out data or non-held-out classes?

I'm Spanish and I don't understand the meaning of "non-held-out". I have tried Google Translator and online dictionaries like Longman but I can't find a suitable translation for this term. You can ...
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1answer
26 views

What does “shape information” mean in terms of GAN(generative adversarial networks)?

A paper says However, annotations used as inputs to C-GAN are typically based only on shape information, which can result in undesirable intensity distributions in the resulting artificially-...
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2answers
135 views

How can reinforcement learning be unsupervised learning if it uses deep learning?

I was watching a video in my online course where I'm learning about A.I. I am a very beginner in it. At one point in the course, the instructor says that reinforcement learning (RL) needs a deep ...
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2answers
67 views

Are bandits considered an RL approach?

If a research paper uses multi-armed bandits (either in their standard or contextual form) to solve a particular task, can we say that they solved this task using a reinforcement learning approach? Or ...
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2answers
2k views

What is feature embedding in the context of convolutional neural networks?

What are feature embeddings in the context of convolutional neural networks? Is it related to bottleneck features or feature vectors?
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1answer
40 views

How is an architecture composed of a second model that validates the first one called in machine learning?

I have a mix of two deep models, as follows: if model A is YES --pass to B--> if model B is YES--> result = YES if model A is NO ---> result = NO So ...
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4answers
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What is the difference between latent and embedding spaces?

In general, the word "latent" means "hidden" and "to embed" means "to incorporate". In machine learning, the expressions "hidden (or latent) space" and "embedding space" occur in several contexts. ...
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3answers
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What is the concept of Tensorflow Bottlenecks?

What is the concept and how does one calculate Bottleneck values? How do these values help image classification? Please explain in simple words.
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What are bottleneck features?

In the blog post Building powerful image classification models using very little data, bottleneck features are mentioned. What are the bottleneck features? Do they change with the architecture that is ...