Questions tagged [comparison]

For questions that involve the comparison of two AI concepts, terms or expressions. An example of such a question is: how does machine learning compare to deep learning?

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3
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1answer
49 views

When to use the state value function $V(s)$ and when to use the state-action value function $Q(s, a)$?

I saw the difference between value function $V(s)$ and $Q(s, a)$. But when do I use each one? When I coded in Matlab I only used $Q(s, a)$ directly (as I was thinking of a tabular approach). So, when ...
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1answer
38 views

How will MLOps and lifelong learning be complementary?

According to [1], in MLOps, continuous training is a new property, unique to ML systems, that's concerned with automatically retraining and serving the models. While lifelong/incremental learning ...
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0answers
49 views

What is the difference between Probabilistic Graphical models and Graph Neural networks?

While going over PGMs and GNNs, it seems like both leverage the graph data structure. The former has been used to represent causal associations (among other things), while the latter has a varied set ...
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1answer
65 views

How is the VAE related to the Autoencoding Variational Bayes (AEVB) algorithm?

I am familiar with the variational autoencoder, but not totally clear on what exactly the AEVB is. In the original VAE paper (by Kingma and Welling), he uses both the terms variational autoencoder and ...
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1answer
26 views

In this example of fuzzy c-means, what is the difference between "sigma" and "center" for the clusters?

In this example, what exactly do "Cluster" and "Sigma" mean? (They chose random coordinates for the three centroids of the groups) Centers: Cluster centers, returned as a ...
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0answers
23 views

What is the difference between "Syllogism" and "Law of Syllogism"?

The logical arguments are the basis for Artificial Intelligence. That is why I picked AI community to ask my question. Reading from Wikipedia, A syllogism is a kind of logical argument that applies ...
3
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1answer
71 views

Closed networks vs Networks with a removed delay to predict new data

I've come across two types of neural networks to predict, both from Matlab, the closed structure and the net that removes one delay to find new data. From Matlab's app generated scripts we see: % ...
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2answers
169 views

When to use Value Iteration vs. Policy Iteration

Both value iteration and policy iteration are General Policy Iteration (GPI) algorithms. However, they differ in the mechanics of their updates. Policy Iteration seeks to first find a completed ...
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1answer
52 views

When should we use CNN instead of MLP?

Is CNN only applicable to time-series data or image data? When should we use CNN instead of MLP?
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1answer
67 views

Why should one ever use ReLU instead of PReLU?

To me, it seems that PReLU is strictly better than ReLU. It does not have the dying ReLU problem, it allows negative values and it has trainable parameters (which are computationally negligible to ...
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1answer
69 views

Can I treat "experience" in reinforcement learning as "training data" in statistical learning?

Statistics is a branch of mathematics that extracts useful information from data. The data is generally called as "training data" in statistical (machine) learning. Consider the following ...
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1answer
32 views

Can I always interpret features as random variables in machine learning safely?

Consider the following statements from Chapter 5: Machine Learning Basics from the book titled Deep Learning (by Aaron Courville et al.) Machine learning tasks are usually described in terms of how ...
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1answer
306 views

What is the difference between the US and global edition of the AIMA book by Russell and Norvig?

The book Artificial Intelligence: A Modern Approach by Russell and Norvig has two editions: global and the US. It looks like these two are generally the same, but have some differences in the order of ...
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1answer
28 views

What is the borderline between unsupervised learning and regular algorithms?

Unsupervised learning using neural networks is clearly machine learning since it is utilising neural nets. However, some algorithms, k-means clustering, for example, are considered unsupervised ...
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1answer
65 views

A comparison of Expert Systems and Machine Learning approaches in terms of run-time-efficiency and time/space complexity

For part of a paper I am writing on Clinical Decision Support Systems (computer-aided medical decision making, e.g. diagnosis, treatment), I am trying to compare Expert Systems with systems based on ...
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Why does one-step TD strengthen only the last action of the sequence of actions that led to the high reward, while n-step TD the last n actions?

In the caption of figure 7.4 (p. 147) of Sutton & Barto's book (2nd edition), it's written The one-step method strengthens only the last action of the sequence of actions that led to the high ...
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1answer
18 views

Why would the Dice coefficient be more suitable than mutual information when you don't want 0-0 matches to be significant?

I'm confused about the interpretation and assumptions of the Dice coefficient versus the more popular measure mutual information. I'm specifically referencing its use in hierarchical semantic network ...
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1answer
82 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 by Ian J. Goodfellow et al. We simultaneously train G ...
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16 views

Is there any difference between conditional batch normalization and batch normalization except the usage of MLPs for predicting $\beta$ and $\gamma$?

Batch normalization in neural networks uses $\beta$ and $\gamma$ for scaling. The analytical formula is given by $$\dfrac{x - \mathbb{E}[x]}{\sqrt{Var(X)}}* \gamma + \beta$$ Conditional batch ...
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1answer
61 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
44 views

Does average loss function in GAN training is just an approximation of value function and does not ensure convergence of generator and discriminator?

The value function on which convergence has been proved by the original paper of GAN is $$\min_G \max_DV(D, G) = \mathbb{E}_{x ∼ P_{data}}[\log D(x)] + \mathbb{E}_{z ∼ p_z}[log (1 - D(G(z)))]$$ and ...
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0answers
8 views

Dose the input channels of depth-wise convolution must be same with output channels?

Just learned the concept of group convolution and depth-wise convolution, I am confused what the difference between depth-wise and group convolution is. Does the input channels of depth-wise ...
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1answer
48 views

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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2answers
64 views

Why not make the training set and validation set one if their roles are similar?

If the validation set is used to tune the hyperparameters and the training set adjusts the weights, why don't they be one thing as they have a similar role, as in improving the model?
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23 views

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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1answer
115 views

In Computer Vision, what is the difference between a transformer and attention?

Having been studying computer vision for a while, I still cannot understand what the difference between a transformer and attention is?
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1answer
30 views

What is the advantage of RL compared with my simple classic algorithm for the MountainCarEnv?

What is the advantage of RL compared with the following simple classic algorithm for the MountainCarEnv? Considering that it takes a long time to train the agent ...
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0answers
33 views

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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0answers
23 views

Are there forms of AI not based on a program and, separated from it, a set of data?

Artificial intelligence is based on programs and data on which the program operates. Human intelligence is brain-based. There is no physical separation between program and data. The state of the ...
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0answers
26 views

Is the main difference between the logistic regression and the perceptron the activation function they use?

I went through a Stats StackExchange's post about the difference between logistic regression and perceptron, which is too long to get the key point. I'd like to consider the question in terms of the ...
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1answer
64 views

What are (all) the differences between a neuron and a perceptron?

I know two differences between a neuron and a perceptron Neuron employs non-linear activation function and perceptron employs only a threshold activation function. Output of a neuron is not ...
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2answers
192 views

What is the difference between a reward and a value for a given state?

I am trying to learn reinforcement learning and I am focusing on the value iteration. I am looking at the example of grid world, and I am trying to implement it in python. While doing this, I ...
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0answers
23 views

What is the conceptual difference between convolutional neural networks and auto-encoders?

I'm familiar with Auto-Encoders and I'm about to dive into CNNs. By having a look at the most important component of a CNN, the filter: I wonder how it is different from Auto-Encoders: For me, it ...
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1answer
47 views

Is the Bandit Problem an MDP?

I've read Sutton and Barto's introductory RL book. They define a policy as a mapping from states to probabilities of selecting each possible action. If the agent is following policy $\pi$ at time $t$, ...
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2answers
67 views

What is the exact difference between distributional semantics and distributed semantics?

While studying word embeddings in natural language processing, I encountered the following statement on page 327 of the textbook Natural Language Processing by Jacob Eisenstein Distributional ...
2
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1answer
264 views

What is the difference between terminal state, nonterminal states and normal states?

In Sutton & Barto's Reinforcement Learning: An Introduction, page 54, the authors define the terminal state as following: Each episode ends in a special state called the terminal state But the ...
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2answers
193 views

What is the difference between "ground truth" and "ground-truth labels"?

I'm aware that the ground-truth of the example at the top left-hand corner of the image below is "zero" However, I am confused about the meaning of the terms ground truth and ground-truth ...
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1answer
46 views

Is categorical encoding a type of word embedding?

Word embedding refers to the techniques in which a word is represented by a vector. There are also integer encoding and one-hot encoding, which I will collectively call categorical encoding. I can see ...
3
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1answer
119 views

Where do the feature extraction and representation learning differ?

Feature selection is a process of selecting a subset of features that contribute the most. Feature extraction allows to get new features that are not actually present in the given set of features. ...
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0answers
80 views

What is the difference between ERL and EA by considering it as RL?

I am currently studying as an MSCS student and my research is based on Evolutionary Algorithm as Reinforcement Learning, and I am confused about the following terms: What is the difference between ...
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1answer
60 views

What is the difference between the "equal error rate" and "detection cost function" metrics?

I was designing a multi-speaker identification model, so I searched for some metrics that one may use. I found two metrics: EER (equal error rate) DCF (detection cost function) What is the ...
4
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1answer
163 views

What are the major differences between multi-armed bandits and the other well-known algorithms (DQN, A3C, PPO, etc)?

I have studied in the past different algorithms, i.e. DQN, DDQN, REINFORCE, A3C, PPO, TRPO, so on. I am doing an internship this summer where I have to use a multi-armed bandit (MAB). I am a bit ...
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2answers
47 views

What is the difference between applying shallow-learning methods repeatedly and deep learning?

In the book Deep Learning with Python, François Chollet writes (section 1.2.6, page 18) In practice, there are fast-diminishing returns to successive applications of shallow-learning methods, because ...
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0answers
76 views

Comparing heuristics in A* search and rescue operation

I was reading a research paper titled A Comparative Study of A-star Algorithms for Search and rescue in Perfect Maze (2011). I have some doubts regarding it: 1. The Evaluation Function of $\mathrm{A}^...
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1answer
756 views

What are the consequences of layer norm vs batch norm?

I'll start with my understanding of the literal difference between these two. First, let's say we have an input tensor to a layer, and that tensor has dimensionality $B \times D$, where $B$ is the ...
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0answers
16 views

Are the two do-calculus systems equivalent?

The version of the do-calculus system in The Book of Why by Judea Pearl is a little bit simpler than the original version. Are the two systems equivalent? The simple version is as follows: Rule1 ...
4
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1answer
84 views

What is the relation between self-taught learning and transfer learning?

I am new to transfer learning and I start by reading A Survey on Transfer Learning, and it stated the following: according to different situations of labeled and unlabeled data in the source domain, ...
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3answers
132 views

Is there a relationship between Computer Algebra and NLP?

My intuition is that there is some overlap between understanding language and symbolic mathematics (e.g. algebra). The rules of algebra are somewhat like grammar, and the step-by-step arguments get ...
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2answers
1k views

What is the difference between a language model and a word embedding?

I am self-studying applications of deep learning on the NLP and machine translation. I am confused about the concepts of "Language Model", "Word Embedding", "BLEU Score". ...
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0answers
42 views

Which method is the most efficient for memory-bounded MCTS with a transposition table?

I am building an agent for a board game that can have a relatively lot of time to think. Therefore, memory management should be efficient. I am using a transposition table, where the nodes are stored ...

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