All Questions
Tagged with difference or comparison
458 questions
2
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0
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119
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When to model decision-making problem as single agent vs multi-agent problem?
I understand the goals and purposes of RL in the case of a single agent and the underlying model, i.e. MDPs, for RL problems (or sequential decision making with uncertainty in general).
My question is ...
- 121
8
votes
2
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10k
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What is the difference between a loss function and reward/penalty in Deep Reinforcement Learning?
In Deep Reinforcement Learning (DRL) I am having difficulties in understanding the difference between a Loss function, a reward/penalty and the integration of both in DRL.
Loss function: Given an ...
- 195
3
votes
1
answer
2k
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What is the difference between a greedy policy and an optimal policy?
I am struggling to understand what is the difference between an optimal policy and a greedy policy.
Let $F(r_{t+1},s_{t+1}| s_t,a_t)$ be the probability distribution accorting to which, given action $...
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3
votes
1
answer
2k
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What is multi-head attention doing mathematically, and how is it different from self-attention?
I'm trying to understand the difference between the concept of self-attention and multi-head attention. The latter is not actually too clear to me.
I understand that, in the case of self-attention, we ...
- 307
1
vote
0
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63
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How to compare RL algorithms with different NN sizes?
I wanted to run some tests with some RL algorithms in a continuous control task, namely PPO-clip and SAC.
When comparing their NN structures described in their papers, SAC used 2 layers with 256 ...
- 119
2
votes
1
answer
240
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Is logic AI a complement to learning AI?
I want to know the relation between logic AI and learning AI.
Logic AI here refers to the branch of AI that is based on mathematical logic. Learning AI refers to the branch of AI that is based on ...
- 3,990
1
vote
1
answer
1k
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What is the difference between Mean Teacher and Knowledge Distillation?
I recently read two papers:
BYOL Bootstrap your own latent: A new approach to self-supervised Learning
DINO Emerging Properties in Self-Supervised Vision Transformers.
I am confused about the terms ...
0
votes
1
answer
138
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How many layers and neurons in a FFNN do I need to make it equivalent to a CNN?
I started to learn machine learning early, and I studied the convolutional neural network and its ability to understand images and how it helps to reduce the number of parameters that need to be tuned....
13
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2
answers
2k
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Is there a fundamental difference between an environment being stochastic and being partially observable?
In AI literature, deterministic vs stochastic and being fully-observable vs partially observable are usually considered two distinct properties of the environment.
I'm confused about this because what ...
- 255
2
votes
1
answer
74
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Can teacher forcing in RNN ensure Turing completeness?
RNN has the same capability as a universal Turing machine. But I am confused whether RNN holds the same capabilities if we use teacher forcing.
Consider the following excerpts from paragraphs taken ...
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1
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0
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81
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Is the capability of RNN more than the capability of MLP?
Consider the following excerpt paragraph taken from the section titled "Recurrent Neural Networks" of the chapter 10: Sequence Modeling: Recurrent and Recursive Nets of the textbook named ...
- 3,990
4
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1
answer
255
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Is there any relation between the recursive neural network and recurrent neural network?
Recurrent neural networks, abbreviated as RNNs, are widely used in deep learning literature, especially for text processing.
Are they related to recursive neural networks in any way?
I am asking for ...
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1
vote
1
answer
270
views
Are the capabilities of connectionist AI and symbolic AI the same?
The universal approximation theorem says that MLP with a single hidden layer and enough number of neurons can able to approximate any bounded continuous function. You can validate it from the ...
- 3,990
1
vote
1
answer
384
views
What is meant by "two action selections" in SARSA?
I have some difficulties understanding the difference between Q-learning and SARSA. Here (What are the differences between SARSA and Q-learning?) the following updating formulas are given:
Q-Learning
$...
- 266
5
votes
1
answer
856
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What is the difference between an on-policy distribution and state visitation frequency?
On-policy distribution is defined as follows in Sutton and Barto:
On the other hand, state visitation frequency is defined as follows in Trust Region Policy Optimization:
$$\rho_{\pi}(s) = \sum_{t=0}^...
- 379
6
votes
1
answer
2k
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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 ...
2
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2
answers
208
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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 ...
- 967
2
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0
answers
153
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 ...
- 670
1
vote
1
answer
660
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 ...
- 243
0
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1
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128
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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 ...
- 121
1
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1
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338
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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 ...
- 13
2
votes
1
answer
173
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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:
% ...
6
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2
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8k
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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 ...
- 541
1
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1
answer
349
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?
- 339
5
votes
1
answer
2k
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 ...
- 151
1
vote
1
answer
351
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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1
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1
answer
439
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 ...
- 3,990
8
votes
3
answers
6k
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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-1
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1
answer
42
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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 ...
- 1,318
3
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1
answer
246
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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 ...
- 25
0
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0
answers
29
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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 ...
- 379
0
votes
1
answer
230
views
What is the difference between a vision transformer and image-based relational learning?
I am trying to figure out the difference between the architecture used in this and this paper. It looks like both used multi-headed self-attention and therefore should be the same in principle.
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0
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1
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159
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 ...
- 1
1
vote
1
answer
1k
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 ...
- 3,990
0
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0
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564
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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$$
Conditional batch ...
- 3,990
0
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1
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311
views
Is "kernel" different from "filter" in convolutional neural networks?
Recently I asked a question on how a convolution 2d layer changes an RGB image into a grayscale image. Assume that our task is to convert an RGB image into a grayscale image. I use to believe that ...
- 3,990
0
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1
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237
views
Does average loss function in GAN training is just an approximation of value function and does not ensure convergence of generator and discriminator?
The value function on which convergence has been proved by the original paper of GAN is
$$\min_G \max_DV(D, G) = \mathbb{E}_{x ∼ P_{data}}[\log D(x)] + \mathbb{E}_{z ∼ p_z}[log (1 - D(G(z)))]$$
and ...
- 3,990
0
votes
1
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571
views
Is my understanding on "smooth approximation" correct?
Consider the following details regarding Softplus activation function
$$\text{Softplus}(x) = \dfrac{\log(1+e^{\beta x})}{\beta}$$
SoftPlus is a smooth approximation to the ReLU function and can be
...
- 3,990
2
votes
2
answers
77
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?
- 43
2
votes
1
answer
860
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 ...
- 3,990
12
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1
answer
6k
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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?
- 123
0
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1
answer
136
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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 ...
- 183
2
votes
0
answers
94
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?
...
- 339
1
vote
0
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79
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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 ...
- 217
2
votes
1
answer
4k
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.
The output of a neuron is not ...
- 3,990
1
vote
2
answers
2k
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 ...
- 57
1
vote
0
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64
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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 ...
- 205
4
votes
1
answer
829
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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 $t$, ...
- 225
0
votes
2
answers
829
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 ...
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3
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1
answer
5k
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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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