Questions tagged [terminology]
For questions related to the definition of and use of terminology in the context of Artificial Intelligence
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Are Cellular Neural Networks one type of Neural Networks?
I am researching Cellular Neural Networks and have already read Chua's two articles (1988). In cellular neural networks, a cell is only in relation with its neighbors. So it is easy to use them for ...
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$\frac{P(x_1 \mid y, s = 1) \dots P(x_n \mid y, s = 1) P(y \mid s = 1)}{P(x \mid s = 1)}$ indicates that naive Bayes learners are global learners?
I am currently studying the paper Learning and Evaluating Classifiers under Sample Selection Bias by Bianca Zadrozny. In section 3. Learning under sample selection bias, the author says the following:
...
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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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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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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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Visualizing the Loss Landscape of Neural Nets: Meaning of the word 'filter'?
I found myself scratching my head when I read the following phrase in the paper Visualizing the Loss Landscape of Neural Nets:
To remove this scaling effect, we plot loss functions using filter-wise ...
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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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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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Is the traditional meaning of "strong AI" outmoded?
Traditionally, "strong AI" refers to Artificial General Intelligence, the human mind understood as an algorithm (Searle, Chinese Room) and Artificial Consciousness.
But recent advances in Artificial ...
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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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What is meant by "well-behaved gradient" in this context?
Consider the following statement (from the paper Generative Adversarial Nets) about the success of discriminative models
So far, the most striking successes in deep learning have involved ...
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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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What is meant by Hinton when he refers to "Part-Whole Hierarchies" in his GLOM framework
I was recently reading Hinton's GLOM idea How to represent part-whole hierarchies in a neural network, and I am simply unsure about what exactly he means when he says parsing images into "part-...
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Does Yann LeCun consider k-means self-supervised learning?
I was discussing the topic of self-supervised learning with a colleague. After a while we realized we were using different definitions. That's never helpful.
Both of us were introduced to self-...
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What part of the Vaswani et al. is the "transformer"?
Which part of this is the transformer?
Ok, the caption says the whole thing is the transformer, but that's back in 2017 when the paper was published. My question is about how the community uses the ...
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In Probabilistic Graphical Model (written by Daphne Koller), what's the meaning of "parameter" in representation of the distribution?
I just started to read the PGM book written by Daphne Koller.
In the chapter of Bayesian Network Representation(Chapter 3), there are some descriptions about the standard parameterization of the joint ...
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What is the name of this algorithm that estimates the gradient with an average by sampling from a distribution?
Consider maximizing the function $R(w)$ with parameter $w$ using gradient ascent. However, we don't know the gradient $\nabla_wR(w)$ formula. Now suppose $w$ is sampled from a probability distribution ...
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Where does the hierarchical reinforcement learning framework name "MAXQ" come from?
I've been researching different frameworks for hierarchical RL (mainly options, HAMs, and MAXQ) and noticed that both options and HAMs have names that relate to how they function. I can't seem to find ...
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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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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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What role do distractors play in natural language processing?
I’m doing research on natural language processing (NLP). I’d like to put together my own model. However, I'm running into a concept I am not familiar with, namely, distractors. A google search does ...
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What is the difference between tracking and mapping (TAM) and localization and mapping (LAM)?
In the paper Visual SLAM algorithms: a survey from 2010 to 2016 by Takafumi Taketomi, Hideaki Uchiyama and Sei Ikeda it is mentioned
It should be noted that tracking and mapping (TAM) is used instead ...
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How does Friend-or-Foe Q-learning intuitively work?
I read about Q-Learning and was reading about multi-agent environments. I tried to read the paper Friend-or-Foe Q-learning, but could not understand anything, except for a very vague idea.
What does ...
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What is a continuous-attractor neural network?
I am reading about CANN. However, I do not seem to grasp what it is. Maybe someone who has worked with it can explain it? I found out about it while reading about RatSLAM. I understand that it helps ...
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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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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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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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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 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 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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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 "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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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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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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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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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 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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What do "spatial" and "temporal" mean in the context of image processing?
I am new to image processing. I am trying to understand CNNs from this blog post. Here's an excerpt from that article that mentions these terms.
A ConvNet is able to successfully capture the Spatial ...
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What's mutual exclusivity in meta-learning?
What do we mean by mutual exclusivity of tasks?
This work (E Pan, 21) and this one (M Yin, 20) state that most classification meta-learning algorithms fail for non-mutually exclusive tasks as the ...
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Terminology for the weight of likelihood ratio/score function?
If we estimate the gradient of $f(x)$ using the likelihood ratio/score function, i.e.
$$\nabla f = f^*\dfrac{\partial \log p(x)}{\partial \theta}$$
is there any agreed upon terminology to call "$...
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What is the difference between a performance standard and performance measure?
I am reading AI: A Modern Approach. In the 2nd chapter when introducing different agent types, i.e., reflex, utility-based, goal-based, and learning agents, I understood that all types of agents, ...
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What is the formal terminology for emotion recognition AI?
I'm researching the use of emotion recognition in Intelligent Tutoring Systems and trying to more effectively find and formally reference materials. My question is whether this is the most formal ...
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Does Algorithmic Mechanism Design come under the field of AI?
I see many papers in AAMAS talk about artificial intelligence and mechanism design simultaneously. I was wondering, for the sake of being pedantic, is mechanism design could be classified under AI.
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Does it classify as Machine Learning?
I have a gaussian distributed time series ($X_t$) with some parameters in my experiment. Suppose I want to know the mean $\mu$. If I define another time series $Y_t$ such that $Y_t=X_t-a$ for all $t$. ...
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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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What is the term for an RNN that is a completely connected directed graph?
There seems to be a severe problem with the taxonomy of neural network topologies. What I'd like to know is the term I should use to search for the most general topology: completely connected ...
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What is the difference between Knowledge Representation and Automated Reasoning?
Knowledge Representation and Automated Reasoning are two AI subfields which seem to have something to do with reasoning.
However, I can't find any information online about their relationship. Are ...
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In the context of lenet, does C1 refer to the conv layer or the output of the conv layer?
I'm studying lenet.
C1 is the layer
According to a tutorial, C1 is
the first convolutional layer with 6 convolution kernels of size 5× 5.
C1 is the feature map
However, I believe that the part ...
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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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