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 does the notation $\mathcal{N}(z; \mu, \sigma)$ stand for in statistics?

I know that the notation $\mathcal{N}(\mu, \sigma)$ stands for a normal distribution. But I'm reading the book "An Introduction to Variational Autoencoders" and in it, there is this notation:...
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166 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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510 views

What is a probability distribution in machine learning?

If we were learning or working in the machine learning field, then we frequently come across the term "probability distribution". I know what probability, conditional probability, and ...
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605 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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64 views

What are “development test sets” used for?

This is a theoretical question. I am a newbie to artificial intelligence and machine learning, and the more I read the more I like this. So far, I have been reading about the evaluation of language ...
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126 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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372 views

What are “proxy data sets” in machine learning?

The paper Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration uses the term "proxy data sets" in this way To develop DL ...
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1answer
2k views

What are options in reinforcement learning?

According to a lecture about Reinforcement Learning, the concept of options allows searching the state space of an agent much faster. The lecture came from Nptel [1] (National Program on Technology ...
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3answers
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What is the name of an AI system that learns by trial and error?

Imagine a system that controls dampers in a complex vent system that has an objective to perfectly equalize the output from each vent. The system has sensors for damper position, flow at various ...
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43 views

Is a team of ML scientists an “intelligent agent”?

I am writing about the role of machine learning scientists in developing a solution. Is there a term for the humans who do learning? Can we call a "team of machine learning scientists with their ...
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50 views

What is the type of problem requiring to rate images on a scale?

I'm new to the topic, but I've used some off the shelf knowledge about computer vision for classifying images. For example, you can easily generate labels that can determine whether or not e.g. a ...
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209 views

Is there any difference between a control and an action in reinforcement learning?

There are reinforcement learning papers (e.g. Metacontrol for Adaptive Imagination-Based Optimization) that use (apparently, interchangeably) the term control or action to refer to the effect of the ...
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What is the role of biology in AI?

Biology is used in AI terminology. What are the reasons? What does biology have to do with AI? For instance, why is the genetic algorithm used in AI? Does it fully belong to biology?
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What is an objective function?

Local search algorithms are useful for solving pure optimization problems, in which the aim is to find the best state according to an objective function. My question is what is the objective function?
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894 views

What is a state in a recurrent neural network?

I am Reading "Supervised Sequence Labelling with Recurrent Neural Networks" written by Alex Graves to try to understand LSTM networks and I am a bit confused about the equations. Specifically, what I ...
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1answer
106 views

Are there any DeepQA-based computers other than Watson?

My understanding is that Watson is the name of the computer, and DeepQA is the name of the software or technology. They are both correlated. Are there any computers/technologies other than Watson ...
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150 views

Why are the terms classification and prediction used as synonyms in the context of deep learning?

Why are the terms classification and prediction used as synonyms especially when it comes to deep learning? For example, a CNN predicts the handwritten digit. To me, a prediction is telling the next ...
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61 views

What is the difference between the concepts “known environment” and “deterministic environment”?

According to the book "Artificial Intelligence: A Modern Approach", "In a known environment, the outcomes (or outcome probabilities if the environment is stochastic) for all actions are given.", and ...
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943 views

What are sim2sim, sim2real and real2real?

Recently, I always hear about the terms sim2sim, sim2real and real2real. Will anyone explain the meaning/motivation of these terms (in DL/RL research community)? What are the challenges in this ...
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166 views

What is the difference between image processing and computer vision?

What is the difference between image processing and computer vision? They are apparently both used in artificial intelligence.
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957 views

What is a bad local minimum in machine learning?

What is "bad local minima"? The following papers all mention this expression. Eliminating all bad Local Minima from Loss Landscapes without even adding an Extra Unit limination of All Bad Local ...
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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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54 views

What is Federated Learning?

How would you explain Federated Learning in simple layman terms for a non-STEM person? What are the main ideas behind Federated Learning?
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372 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
65 views

Precise description of one-shot learning

I am working on classifying the Omniglot dataset, and the different papers dealing with this topic describe the problem as one-shot learning (classification). I would like to nail down a precise ...
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52 views

Terminology for the use of datasets as data points

As computers are getting bigger better and faster, the concept of what constitutes a single datum is changing. For example, in the world of pen-and-paper, we might take readings of temperature over ...
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Why do we need learning in unsupervised learning? [duplicate]

I am not clear with the concept that an unsupervised model learns. We are giving an input and output to the supervised model, so that it can generate a particular value, pattern or something out of it ...
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168 views

What is USV In NLP?

3 SVD Based Methods For this class of methods to find word embeddings (otherwise known as word vectors), we first loop over a massive data set and accumulate word co-occurrence counts in some form of ...
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95 views

What is asymmetric relaxation backpropagation?

In Chapter 8, section 8.5.2, Raul Rojas describes how the weights for a layer of a neural network can be calculated using a pseudoinverse of the sigmoid function in the nodes, he explains this is an ...
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56 views

What's the difference between architectures and backbones?

In the paper "ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery", the authors talk about using: Feature Pyramid Networks (as the ...
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60 views

Why are “Transformers” called this way?

What is the reason behind the name "Transformers", for Multi Head Self-Attention-based neural networks from Attention is All You Need? I have been googling this question for a long time, and ...
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What is the meaning of the words 'bias' and 'variance' in RL?

In reinforcement learning approaches, like temporal-difference (TD) learning or Monte Carlo methods, two of the metrics used to measure their performance are the bias and the variance. What do these ...
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90 views

What are the differences between learning by analogy, inductive learning and explanation based learning?

I have heard of the concepts of learning by analogy (which is quite self-explanatory), inductive learning and explanation-based learning. I tried to learn about inductive learning and explanation-...
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3answers
236 views

What is the name of an AI whose primary goal is to create a better AI?

A general AI x creates another AI y which is better than x. y creates an AI better than itself. And so on, with each generation's primary goal to create a better AI. Is there a name for this. By ...
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268 views

Is there any difference between ConvNet and CNN?

ConvNet stands for Convolutional Networks and CNN stands for Convolutional Neural Networks. Is there any difference between both? If yes, then what is it? If no, is there any reason behind using ...
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180 views

Why is it called back-propagation?

While looking at the mathematics of the back-propagation algorithm for a multi-layer perceptron, I noticed that in order to find the partial derivative of the cost function with respect to a weight (...
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1answer
51 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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2answers
80 views

What is the name of this neural network architecture with layers that are also connected to non-neighbouring layers?

Consider a feedforward neural network. Suppose you have a layer of inputs, which is feedforward to a hidden layer, and feedforward both the input and hidden layers to an output layer. Is there a name ...
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1answer
53 views

Would you term Google's Captchas as Turing Test?

Quoting from Wikipedia page on Turing Test The Turing test, developed by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable ...
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1answer
46 views

What is the AI discipline where an algorithm learns from an initial training set, but then refines its learning as it uses that training?

Imagine a system that is trained to manipulate dampers to manage air flow. The training data includes damper state and flow characteristics through a complex system of ducts. The system is then given ...
2
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1answer
81 views

What does “we wrap the individual and reuse the codons” mean in the paper “Grammatical Evolution” by Neill and Ryan?

I've just started learning Grammatical Evolution and I'm reading the paper Grammatical Evolution by Michael O'Neill and Conor Ryan. On page 3 (section IV-A), they write: During the genotype-to-...
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2answers
466 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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1answer
83 views

What are preferences and preference functions in multi-objective reinforcement learning?

In RL (reinforcement learning) or MARL (multi-agent reinforcement learning), we have the usual tuple: (state, action, transition_probabilities, reward, next_state) ...
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132 views

Is my understanding of the value function, Q function, policy, reward and return correct?

I'm a beginner in the RL field, and I would like to check that my understanding of certain RL concepts. Value function: How good it is to be in a state S following policy π. ...
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1answer
562 views

Are model-free and off-policy algorithms the same?

In respect of RL, is model-free and off-policy the same thing, just different terminology? If not, what are the differences? I've read that the policy can be thought of as 'the brain', or decision ...
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2answers
64 views

What does “immediate vector-valued feedback” mean?

In the book Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning, James Stone says With supervised learning, the response to each input vector is an output ...
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1answer
73 views

How does the Kullback-Leibler divergence give “knowledge gained”?

I'm reading about the KL divergence on Wikipedia. I don't understand how the equation gives "information gained" as it says in the "Interpretations" section Expressed in the ...
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1answer
82 views

Biological analogy for boosting and inhibition idea in Hierarchical Temporal Memory (HTM)

I've just watched the 9th episode of HTM school about the "boosting" and "inhibition" ideas. However, I couldn't find the neuroscience counterpart of these terms and concepts. Since HTM is a ...
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49 views

What do we mean by “infrequent features”?

I am reading this blog post: https://ruder.io/optimizing-gradient-descent/index.html. In the section about AdaGrad, it says: It adapts the learning rate to the parameters, performing smaller updates (...
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52 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? ...