All Questions
Tagged with comparison comparison or
458 questions
4
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What is the difference between GAT and GaAN?
I was looking at two papers
Graph Attention Networks (GAT) by Petar Veličković and
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs by Jiani Zhang.
I'm trying to ...
- 1,158
4
votes
2
answers
927
views
How to make a fair comparison of a convolutional neural network (cNN) vs a mutlilayer perceptron (MLP)?
I'm working with deep learning on some EEG data for classification, and I was wondering if there's any systematic/mathematical way to define the architecture of the networks, in order to compare their ...
3
votes
1
answer
4k
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What are the differences between Q-Learning and A*?
Q-learning seems to be related to A*. I am wondering if there are (and what are) the differences between them.
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3
votes
4
answers
315
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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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3
votes
4
answers
896
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What are the pros and cons of studying machine learning before deep learning? [duplicate]
I'm a biotech student and I'm currently working on single-particle tracking. For my work, I need to use aspects of deep learning (CNN, RNN and object segmentation) but I'm not familiar with these ...
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3
votes
4
answers
248
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What is the difference between artificial intelligence and artificial neural networks?
I have made several neural networks by using Brain.Js and TensorFlow.js.
What is the difference between artificial intelligence and artificial neural networks?
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3
votes
2
answers
4k
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What is the difference between A2C and Q-Learning, and when to use one over the other?
I'm trying to get an accurate answer about the difference between A2C and Q-Learning. And when can we use each of them?
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3
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3
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1k
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Why are Siamese Neural Networks used instead of a single neural network?
Siamese Neural Networks are a type of neural network used to compare two instances and infer if they belong to the same object. They are composed by two parallel identical neural networks, whose ...
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3
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2
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109
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How to distinguish AI modeling from implementation?
Quote from this Eric's meta post about modelling and implementation:
They are not exactly the same, although strongly related. This was a very difficult lesson to learn among mathematicians and early ...
- 10.5k
3
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1
answer
339
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How can a probability density value be used for the likelihood calculation?
Consider our parametric model $p_\theta$ for an underlying probabilistic distribution $p_{data}$.
Now, the likelihood of an observation $x$ is generally defined as $L(\theta|x) = p_{\theta}(x)$.
The ...
- 3,990
3
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2
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861
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Apart from the state and state-action value functions, what are other examples of value functions used in RL?
In reinforcement learning, we often define two functions, the state-value function
$$V^\pi(s) = \mathbb{E}_{\pi} \left[\sum_{k=0}^{\infty}
\gamma^{k}R_{t+k+1} \Bigg| S_t=s \right]$$
and the state-...
- 41.4k
3
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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
1k
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How to express $v_\pi(s)$ in terms of $q_\pi(s,a)$?
This is exercise 3.18 in Sutton and Barto's book.
The task is to express $v_\pi(s)$ using $q_\pi(s,a)$.
Looking at the diagram above, the value of $q_\pi(s,a)$ at $s$ for each $a \in A$ we take gives ...
- 392
3
votes
1
answer
2k
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Why do we need convolutional neural networks instead of feed-forward neural networks?
Why do we need convolutional neural networks instead of feed-forward neural networks?
What is the significance of a CNN? Even a feed-forward neural network will able to solve the image classification ...
- 1,763
3
votes
2
answers
939
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What is the difference between reinforcement learning and AutoML?
My vague understanding of reinforcement learning (RL) is that it's very similar to supervised learning except that it updates on a continuous feed of data/activity, this to me sounds very similar to ...
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3
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1
answer
3k
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What are the major differences between cost, loss, error, fitness, utility, objective, criterion functions?
I find the terms cost, loss, error, fitness, utility, objective, criterion functions to be interchangeable, but any kind of minor difference explained is appreciated.
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2
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2k
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What is the difference between human brains and neural networks? [duplicate]
There are many people trying to show how neural networks are still very different from humans, but I fail to see in what way human brains are different from neural models in anything but complexity.
...
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3
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2
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884
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When should I use Genetic Algorithms as opposed to Particle Swarm Optimization?
For which problems are Genetic Algorithms more suitable than Particle Swarm Optimization, and vice-versa? Are there any guidelines?
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3
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1
answer
589
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Do the terms 'sample complexity' and 'sample efficiency' mean the same thing in RL context
For example, the the paper Soft Actor-Critic:Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor, both terms are mentioned but without explaining. I have seen them in other ...
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3
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1
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2k
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How are these equations of SGD with momentum equivalent?
I know this question may be so silly, but I can not prove it.
In Stanford slide (page 17), they define the formula of SGD with momentum like this:
$$
v_{t}=\rho v_{t-1}+\nabla f(x_{t-1})
\\
x_{t}=x_{...
- 912
3
votes
1
answer
2k
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How fast does Monte Carlo tree search converge?
How fast does Monte Carlo Tree Search converge? Is there a proof that it converges?
How does it compare to temporal-difference learning in terms of convergence speed (assuming the evaluation step is a ...
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3
votes
2
answers
323
views
What is the difference between a normal processor and a processor designed for AI?
What is the difference between a normal processor and a processor designed for AI?
user9947
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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3
votes
1
answer
1k
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How is the reward in reinforcement learning different from the label in supervised learning problems?
How is the notion of immediate reward used in the reinforcement learning different from the notion of a label we find in the supervised learning problems?
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3
votes
1
answer
5k
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What is the difference between hill-climbing and greedy best-first search algorithms?
While watching MIT's lectures about search, 4. Search: Depth-First, Hill Climbing, Beam, the professor explains the hill-climbing search in a way that is similar to the best-first search. At around ...
- 1,291
3
votes
2
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3k
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Why do value iteration and policy iteration obtain similar policies even though they have different value functions?
I am trying to implement value and policy iteration algorithms. My value function from policy iteration looks vastly different from the values from value iteration, but the policy obtained from both ...
- 335
3
votes
1
answer
380
views
Is RL just a less rigorous version of stochastic approximation theory?
After reading some literature on reinforcement learning (RL), it seems that stochastic approximation theory underlies all of it.
There's a lot of substantial and difficult theory in this area ...
- 847
3
votes
3
answers
278
views
What is the difference between batch and mini-batch gradient decent?
I am learning deep learning from Andrew Ng's tutorial Mini-batch Gradient Descent.
Can anyone explain the similarities and dissimilarities between batch GD and mini-batch GD?
- 1,763
3
votes
1
answer
147
views
Which is a better form of regularization: lasso (L1) or ridge (L2)?
Given a ridge and a lasso regularizer, which one should be chosen for better performance?
An intuitive graphical explanation (intersection of the elliptical contours of the loss function with the ...
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3
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1
answer
183
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What are the differences between TensorFlow and PyTorch? [closed]
What are the differences between TensorFlow and PyTorch, both in terms of performance and functionality?
3
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2
answers
141
views
What are the advantages of Machine Learning compared to traditional programming for developing a chatbot?
I am currently building a chatbot. What I have done so far is, collected possible questions/training data/files and create a model out of it using Apache OpenNLP; the model is able to predict all the ...
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3
votes
1
answer
230
views
What is the relationship between degrees of freedom and the size of the training dataset?
I am going through the book Pattern Recognition by Bishop.
At one point he says
For $M = 9$, the training set error goes to zero, as we might expect because this polynomial contains 10 degrees of ...
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3
votes
1
answer
230
views
What is the relationship between MLE and naive Bayes?
I have found various references describing Naive Bayes and they all demonstrated that it used MLE for the calculation. However, this is my understanding:
$P(y=c|x)$ $\propto$ $P(x|y=c)P(y=c)$
with $...
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3
votes
1
answer
342
views
Do eligibility traces and epsilon-greedy do the same task in different ways?
I understand that in Reinforcement Learning algorithms, such as Q-learning, in order to prevent selecting the actions with greatest q-values too fast and allow for exploration, we use eligibility ...
3
votes
1
answer
535
views
What are the main differences between a deep Boltzmann machine and a deep belief network?
What are the main differences between a deep Boltzmann machine (DBM) (a recurrent neural network) and a deep belief network (which is based on RBMs)?
- 10.5k
3
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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
3
votes
1
answer
4k
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How does best-first search differ from hill-climbing?
How does best-first search differ from hill-climbing?
3
votes
2
answers
627
views
Why are the terms classification and prediction used as synonyms in the context of deep learning?
Why are the terms classification and prediction used as synonyms especially when it comes to deep learning? For example, a CNN predicts the handwritten digit.
To me, a prediction is telling the next ...
- 445
3
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1
answer
737
views
What is the difference between the concepts "known environment" and "deterministic environment"?
According to the book "Artificial Intelligence: A Modern Approach", "In a known environment, the outcomes (or outcome probabilities if the environment is stochastic) for all actions are given.", and ...
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3
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1
answer
813
views
Can non-sequential deep learning models outperform sequential models in time series forecasting?
Can a CNN (or other non-sequential deep learning models) outperform LSTM (or other sequential models) in time series data?
I know this question is not very specific, but I experienced this when ...
3
votes
1
answer
525
views
What is the difference between the ant system and the max-min ant system?
I'm studying ant colony optimization. I'm trying to understand the difference between the ant system (AS) and the max-min ant system (MMAS) approaches. As far as I found out, the main difference ...
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3
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1
answer
332
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What are the differences between stability and convergence in reinforcement learning?
The terms are mentioned in the paper: An Emphatic Approach to the Problem of off-Policy Temporal-Difference Learning (Sutton, Mahmood, White; 2016) and more, of course.
In this paper, they proposed ...
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3
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1
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704
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What is the difference between image processing and computer vision?
What is the difference between image processing and computer vision? They are apparently both used in artificial intelligence.
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1
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466
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How to transform a PDDL to search?
I have a question about search and planning:
I still haven't understood the difference from the two, but they seem very similar to me; here is a question I am struggling with:
"Having formulated a ...
- 273
3
votes
1
answer
139
views
Is explainable AI more feasible through symbolic AI or soft computing?
Is explainable AI more feasible through symbolic AI or soft computing?
How much each paradigm, symbolic AI and soft computing (or hybrid approaches), addresses explanation and argumentation, where ...
3
votes
2
answers
185
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What is the difference between automatic transcription and automatic speech recognition?
What is the difference between automatic transcription and automatic speech recognition? Are they the same?
Is my following interpretation correct?
Automatic transcription: it converts the speech to ...
- 141
3
votes
2
answers
315
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What are the strengths of the Hierarchical Temporal Memory model compared to competing models?
What are the strengths of the Hierarchical Temporal Memory model compared to competing models such as 'traditional' Neural Networks as used in deep learning? And for those strengths are there other ...
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3
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1
answer
3k
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Why is the hyperbolic tangent with MSE better than the sigmoid with cross-entropy?
Usually, in binary classification problems, we use sigmoid as the activation function of the last layer plus the binary cross-entropy as cost function.
However, I have already experienced (more than ...
3
votes
1
answer
9k
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Does SAC perform better than PPO in sample-expensive tasks with discrete action spaces?
I am currently using Proximal Policy Optimization (PPO) to solve my RL task. However, after reading about Soft Actor-Critic (SAC) now I am unsure whether I should stick to PPO or switch to SAC. ...
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3
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1
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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 ...
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