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 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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1answer
4k views

What is the difference between a receptive field and a feature map?

In a CNN, the receptive field is the portion of the image used to compute the filter's output. But one filter's output (which is also called a "feature map") is the next filter's input. What's the ...
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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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1answer
99 views

What do we mean by 'principal angle between subspaces'?

I came across the term 'principal angle between subspaces' as a tool for comparing objects in images. All material that I found on the internet seems to deal with this idea in a highly mathematical ...
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24 views

When can we call a feature "hierarchial"?

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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1answer
580 views

What does "episodic training" mean?

I'm reading the book Hands-On Meta Learning with Python, and in Prototypical networks said: So, we use episodic training—for each episode, we randomly sample a few data points from each class in ...
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1answer
348 views

Which tasks are called as downstream tasks?

The following paragraph is from page no 331 of the textbook Natural Language Processing by Jacob Eisenstein. It mentions about certain type of tasks called as downstream tasks. But, it provide no ...
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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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1answer
890 views

Are mult-adds and FLOPs equivalent?

I am comparing different CNN architectures for edge implementation. Some papers describing architectures refer to mult-adds, like the MobileNet V1 paper, where it is claimed that this net has 569M ...
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1answer
58 views

Is "kernel" different from "filter" in convolutional neural networks?

Recently I asked a question on how a convolution 2d layer changes an RGB image into a grayscale image. Assume that our task is to convert an RGB image into a grayscale image. I use to believe that ...
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1answer
54 views

Is "width of a neural network" a wrong phrase?

Depth of the neural network is equal to the total number of layers in the neural network except input layer. so, neural network with more number of layers are called deep neural networks. Width, in ...
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1answer
113 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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130 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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90 views

What is the difference between environment states and agent states in terms of Markov property?

I'm going through the David Silver RL course on YouTube. He talks about environment internal state $S^e_t$, and agent internal state $S^a_t$. We know that state $s$ is Markov if $$\mathbb{P}\{S_t=s|S_{...
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95 views

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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1answer
119 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 ...
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2answers
261 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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32 views

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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12 views

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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1answer
242 views

What does "linear unit" mean in the names of activation functions?

Activation functions, in neural networks, are used to introduce non-linearity. Many activation functions that are used in neural networks have the term "Linear Unit" in their full form. &...
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757 views

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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1answer
34 views

What is the difference between “AI Methods” and “AI Techniques”?

These are words that we frequently come upon. What can be said about the differences? Would these two words' subheadings be different?
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25 views

Can I always use "encoding" and "embedding" interchangeably?

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

What do you call a machine learning system that keeps on learning?

As I understand it from this video lecture, there are three types of deep learning: Supervised Unsupervised Reinforcement All these can serve to train a neural network either only prior to its ...
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49 views

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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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. Should I understand ...
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1answer
105 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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196 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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What are "bottlenecks" in neural networks?

What are "bottlenecks" in the context of neural networks? This term is mentioned, for example, in this TensorFlow article, which also uses the term "bottleneck values". How does ...
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37 views

What is meant by a "relevant document" in NLP?

In natural language processing, I came across the concept of "relevant document" several times. And several analytical formulas, such as precision, recall are based on the relevant documents....
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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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3answers
74 views

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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1answer
18 views

Is there any difference between the phrases "text representation" and "text feature representation"?

Text representation, in simple words, is representing text in sensible numeric form. You can read in detail from the following paragraph Text representation is one of the fundamental problems in text ...
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43 views

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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33 views

What is the fundamental difference between the synthesis task and sampling task?

Among the list of tasks in machine learning, synthesis and sampling is one of the key task. Consider the following explanation regarding synthesis and sampling task from Chapter 5: Machine Learning ...
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24 views

Why not undefined expression is different from numerical underflow?

Consider an architecture or programming language that uses $n$ bits for storing a floating point number in a particular format. Then each and every floating point number it can store should be in a ...
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1answer
61 views

What does 'channel' mean in the case of an 1D convolution?

While reading about 1D-convolution in PyTorch, I encountered the concept of channels ...
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1answer
200 views

What is $USV^T$ in the context of word embeddings?

Here is an excerpt from the notes of the first lecture of the course CS224n: Natural Language Processing with Deep Learning. 3 SVD Based Methods For this class of methods to find word embeddings (...
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2answers
80 views

Why does critical points and stationary points are used interchangeably?

Consider the following paragraph form Numerical Computation of deep learning book. When $f'(x) = 0$, the derivative provides no information about which direction to move. Points where $f'(x)$ = 0 are ...
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2answers
53 views

Where can I read about the multinoulli distribution?

I encountered the term multinoulli distribution in the following sentence from Chapter 4: Numerical Computation of the deep learning book. The softmax function is often used to predict the ...
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1answer
260 views

What is MNLI-(m/mm)?

I came across the term MNLI-(m/mm) in Table 1 of the paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. I know what MNLI stands for, i.e. Multi-Genre Natural ...
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What is artificial intelligence?

What is the definition of artificial intelligence?
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Assume 120 examples, a model makes 20 correct predictions and updates weight for the other 100. Should I count this epoch 100 iterations or 120?

Per google's glossary, an iteration refers to A single update of a model's weights during training ... The following code comes from a github repo ...
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What is the difference between search and planning?

I'm studying Artificial Intelligence. A Modern Approach, Stuart Russell, Peter Norvig, specifically about search and planning arguments. I don't understand the difference between the two terms. I was ...
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1answer
31 views

What exactly is a grid-like topology according to the book Deep Learning?

I am reading this book called "Deep Learning" (by Goodfellow, Bengio and Courville). On page 326, in the first paragraph, it says: CNNs, are a specialized kind of neural network for ...
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1answer
45 views

What does "differentiable architecture" mean?

I'm currently reading a paper that uses CNN's as a base approach to solving some image classification issues and I've found that they kept mentioning the term "Differentiable Architecture", ...
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1answer
70 views

Is there any difference between an objective function and a value function?

I found the usage of both objective function and value function in the same context. Context #1: In the paper titled Generative Adversarial Nets We simultaneously train G to minimize $\log(1 −D(G(z)))...
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1answer
45 views

What does it mean by strong or sufficient gradient for training in this context?

It has been mentioned in the research paper titled Generative Adversarial Nets that generator need to maximize the function $\log D(G(z))$ instead of minimizing $\log(1 −D(G(z)))$ since the former ...
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1answer
113 views

What is numerical stability?

I came across the phrase "numerical stability" several times. But almost in the same context. I encountered this word mostly in the analytical formula for batch normalization. $$y = \dfrac{...
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8k views

What is the definition of "soft label" and "hard label"?

In semi-supervised learning, there are hard labels and soft labels. Could someone tell me the meaning and definition of the two things?

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