Questions tagged [definitions]
For questions about the definition of terms used in artificial intelligence research and development, including the definition of intelligence, algorithms, jargon, principles, methodologies, mathematical terms, concepts, topologies, architectures, designs, jargon, and AI domains such as robotics, network training, or automated vehicles.
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What is 'system card'?
What is 'system card' in these context:
https://ai.meta.com/blog/system-cards-a-new-resource-for-understanding-how-ai-systems-work/
Additionally, individual model developers may provide ...
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What is the concept of pruning a tree in Machine Learning regression problems?
What is the concept of pruning a tree in Machine Learning regression problems? I am confused and a simple explanation would be great.
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Can manual feature extraction be considered a part of a learning algorithm?
A learning algorithm is a tuple $(\mathcal{H}, \mathcal{O}, \mathcal{L})$ where $\mathcal{H}$, $\mathcal{O}$ and $\mathcal{L}$ are the hypothesis class, optimizer and loss function respectively.
We ...
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What is Explainable AI and what does it strive for?
I understand the need for Explainability in AI. However, I am uncertain of what is meant by 'making AI explainable'.
What needs to be explainable? Is it the output of a model? Does it refer to the ...
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How to interpret Tom Mitchell's definition of machine learning?
I quote the well known definition:
A computer program is said to learn from experience E with respect to some class of tasks T and some performance measure P, if its performance on T, as measured by ...
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Why there are only three machine learning paradigms: supervised, unsupervised, reinforcement?
I read in books, blogs, and articles that there are three learning paradigms: supervised, unsupervised, and reinforcement.
However, I have never found a proof that this list is exhaustive. Can it be ...
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Filter distribution of Latent variable models
In this paper https://arxiv.org/pdf/1907.00953.pdf, about stochastic latent variable models, the paper says "We use the
reparameterization trick to sample from the filtering distribution". I ...
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Is AI just a bunch of library functions?
Recently, I began reading more about AI and took a few basic courses to learn the basics of how it works. I also started a few projects involving AI, but get bored very quickly. To me, it feels like ...
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In a Recurrent Neural Network, what are the inputs to a node in a mutli-layer RNN?
I'm trying to work through a project where I'm writing my own RNN in C++ - not using any libraries. Basically I have an Input layer - 2 hidden layers - and then an output layer. In a given layer, each ...
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What is 'fairness' in machine learning?
How does one define the concept of fairness in machine learning? I've seen the term lots of times but never used it myself in research (1, 2). Is there a generally agreed-upon definition of fairness ...
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What is the definition of Q when the discount factor depends on time?
Suppose I want to find out the Q value of a particular state $s$ bu doing action $a$ at a particular timestep $t$. I know that the Q-value when the discount factor is given by,
$$Q(a,s)=E_{\pi}\big[...
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What is the difference between self-supervised and unsupervised learning?
What is the difference between self-supervised and unsupervised learning? The terms logically overlap (and maybe self-supervised learning is a subset of unsupervised learning?), but I cannot pinpoint ...
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What's the difference between Reliability, Resiliency, and Robustness?
In the context of the Machine Learning model, is there any clear definition of reliability, resiliency, and robustness of a model? I saw some papers discuss different things (e.g. attacked model, ...
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Formally, what are the layers in an Artificial Neural Network?
You may not believe it, but I am an ANN expert. Perhaps, for that reason, I am unable to grasp completely what the layers are in a Deep Forward Artificial Neural Network (DFANN).
According to the Deep ...
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Why is the sliding puzzle problem episodic?
Why is the sliding puzzle problem episodic and not sequential?
From what I understand, an environment is episodic if each episode is independent and doesn't affect past or future episodes. The actions ...
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How to understand the definition of $\lambda_i$ used in the return estimator proposed in the paper "Human-level Atari 200x faster"?
I'm reading article called "Human-level Atari 200x faster"
Agent57 uses Retrace (Munos et al., 2016) to compute return estimates
from off-policy data, but we observed that it tends to cut ...
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How to relate the definition for entailment, with soundness and completeness?
Is it fair enough to say for a language model, φ, which makes certain variable A true, and if φ also makes another variable B true, then we can conclude:
A ⊨ B
And for a certain inference calculus c,...
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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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Is my neural network working?
I recently just finished programming a neural network in c#, and it seems like it's working. My question is if I'm doing it right. It's a very confusing process so I will explain.
Basically every ...
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Hot to calculate Maximum Normalized log Probability for Active Learning with BERT
I have encountered difficulties understanding the calculation of Maximum Normalized Log Probabilities acording to Shen et al..
With n being the sequence length, yi the label of word i. Xij is the ...
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134
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Autoencoders: Where does the encoder end and the decoder begin?
Consider a simple Autoencoder neural net:
...
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If a policy is epsilon-greedy, is it technically stochastic?
Even though if exploration doesn't happen, it's deterministic.
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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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What is an epistemic graph in AI and how is it related to cognitive science?
I found this paper Epistemic graphs for representing and reasoning with positive and negative influences of arguments.
I haven't found any definition of or Wikipedia article on epistemic graphs on the ...
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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 exactly is data augmentation?
Data augmentation is useful in training. But, I am not sure when can a modification applied to data can be called data augmentation.
Suppose a technique is applied to the instances of a dataset and ...
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What is the definition of a trace of a tensor?
Tensor is a multi-dimensional ordered collection of elements, which is used in deep learning to store and process data as well as intermediate steps.
We are aware of the trace of a two-dimensional ...
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What is the rigorous and formal definition for the direction pointed by a gradient?
Consider the following definition of derivative from the chapter named Vector Calculus from the test book titled Mathematics for Machine Learning by Marc Peter Deisenroth et al.
Definition 5.2 (...
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When can I call an entity a hyperparameter?
As per my knowledge, any entity that is learnable by a training algorithm can be called a parameter. Weights of a neural network are called parameters because of this reason only.
But I have doubts ...
- 3,612
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What is meant by sub-region of an image?
Consider the following sentences from the research paper titled PatternNet: Visual Pattern Mining with Deep Neural Network by Hongzhi Li et al.
The value of each pixel in a feature map is the ...
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In reinforcement learning, why are policies defined as functions of states and not observations?
I am new to RL and I am following Sutton & Barto's book.
My doubt is, when we talk about the policy of our agent, we say it is the probability of taking some action $a$ given the state $s$. ...
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Can I call any function a signal?
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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What are the definitions for the content and style of an image without using deep neural network?
In deep learning, an image is said to contain two types of features. One is the content of the image and the other is the style of the image.
Deep neural networks are generally used to obtain both ...
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What is the formal definition for manifold in artificial intelligence?
We come across the word "manifold" in artificial intelligence, especially in the domains where learning is done based on data instances.
What is the formal definition for manifold?
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What is meant by correlation structure?
I know only about the Pearson's correlation coefficient in literature.
Covariance between two random variables $X$ and $Y$ is defined as
$$Cov[X, Y] = \mathbb{E}[(X - \mathbb{E}[X])(Y-\mathbb{E}[Y])]$$...
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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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What is the definition of "confidence interval" around a (complicated) function?
Consider the following excerpt from Chapter 5: Machine Learning Basics from the book titled Deep Learning (by Aaron Courville et al.)
Machine learning is essentially a form of applied statistics with ...
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Explaining AI to Non-Technical Individuals
How does one approach proposing AI to management? This is something I have struggled with for a long time. I want to implement AI toward a specific problem in my place of work. My supervisors are ...
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What exactly is a Parzen?
I came across the term "Parzen" while reading the research paper titled Generative Adversarial Nets. It has been used in the research paper in two contexts.
#1: In phrase "Parzen window&...
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Is my understanding on "smooth approximation" correct?
Consider the following details regarding Softplus activation function
$$\text{Softplus}(x) = \dfrac{\log(1+e^{\beta x})}{\beta}$$
SoftPlus is a smooth approximation to the ReLU function and can be
...
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What is meant by an axis of a tensor?
Tensor is an ordered collection of elements. The elements are generally real numbers. Tensors are used in deep learning for storing data.
There is a wide usage of the word "axis" related to ...
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Does "fusion" in "feature fusion" has any formal definition?
I encountered the phrase "fusing features" several times in the literature. I am providing an excerpt from a research paper to provide context for usage of the word fusion.
The reason is ...
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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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Is the formula $\frac {1}{s}\sum _{j=1}^{s}|d_{j}-y_{j}(t)|$ the correct form of 0-1 loss function, in the context of Perceptron?
Per page 7 of this MIT lecture notes, the original single-layer Perceptron uses 0-1 loss function.
Wikipedia uses
$${\displaystyle {\frac {1}{s}}\sum _{j=1}^{s}|d_{j}-y_{j}(t)|} \tag{1}$$
to denote ...
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Why do we commonly use the $\log$ to squash frequencies?
Term frequency and inverse document frequency are well-known terms in information retrieval.
I am presenting the definitions for both from p:12,13 of Vector Semantics and Embeddings
On term frequency
...
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how to go from mathematical problem to neural network (and back)?
I am a little confused on how, you can find online papers that describe complex Machine Learning formulas in a mathematical/probabilistic way, and, in the other hands, easy tutorials that teach you ...
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Fitting a Gaussian distribution into another distribution
Assume we have two vectors, containing random samples (maybe audio data?). Their distribution can be approximated to a normal distribution, so we can calculate their mean and standard deviation.
I am ...
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What makes a transformer a transformer?
Transformers are modified heavily in recent research. But what exactly makes a transformer a transformer? What is the core part of a transformer? Is it the self-attention, the parallelism, or ...
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What exactly are partially observable environments?
I have trouble understanding the meaning of partially observable environments. Here's my doubt.
According to what I understand, the state of the environment is what precisely determines the next state ...
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Why was the VC dimension not defined for all configurations of $d$ points?
Let's start with a typical definition of the VC dimension (as described in this book)
Definition $3.10$ (VC-dimension) The $V C$ -dimension of a hypothesis set $\mathcal{H}$ is the size of the ...
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