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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1answer
7 views

What is the relationship between the reward function and the value function?

To clarify it in my head, the value function calculates how 'good' it is to be in a certain state by summing all future (discounted) rewards, while the reward function is what the value function uses ...
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24 views

What is the difference between using dense layers vs convolutional layers when dealing with images/image data?

I'm working on building a GAN and wondering what is the difference between using dense layers vs convolutional layers in my networks.
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1answer
38 views

What is relation between gradient descent and regularization in deep learning?

Gradient descent is used to reduce the loss and regularization is used to fight over-fitting. Is there any relation between gradient descent and regularization, or both are independent of each other?...
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15 views

What is the difference between training a model with RGB images and using only the color channels separately?

What is the difference between training a model with RGB images and using only the color channels separately (like only the red channel, green channel, etc.)? Would the model also learn patterns ...
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28 views

What are the pros and cons of deep learning and machine learning to develop a trading system?

As I want to start coding a new Trading AI in this year (first based on Python and later maybe in C++) I stumbled over the following question: Today, I would like to make a pro/contra list with you ...
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1answer
19 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?
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0answers
29 views

Are No Free Lunch theorem and Universal Approximation theorem contradictory in the context of neural networks?

To my understanding NFL states that, we cannot have an hypothesis (let's assume it is an approximator like NN in this case) class that can't achieve certain accuracy parameters $\leq \epsilon$ with ...
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1answer
26 views

What are the pros and cons of supervised, semi-supervised and unsupervised relation extraction in NLP?

I am following the NLP course taught by Dan Jurafsky. In the video lectures Supervised Relation Extraction and Semi Supervised and Unsupervised Relation Extraction Jurafsky explains supervised, semi-...
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1answer
32 views

Can neurons in MLP and filters in CNN be compared?

I know they are not the same in working, but an input layer sends the input to x neurons with a set of weights, based of these weights and the activation layer, it produces an output that can be fed ...
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37 views

Which one is better: multivariate regression with basis expansion or neural networks?

Assume we are given a training dataset $D = \{ (x_i, y_i)\}_{i=1}^{N}$. My question is: which is better? A multivariate regression with basis expansion with independent matrix $X$ and dependent ...
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1answer
44 views

What are pros and cons of Bi-LSTM as compared to LSTM?

What are the pros and cons of LSTM vs Bi-LSTM in language modelling? What was the need to introduce Bi-LSTM?
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0answers
16 views

What is the difference between “out-of-distribution (generalisation)” and “(meta)-transfer learning”?

I'm trying to develop a better understanding of the concept of "out-of-distribution" (generalization) in the context of Bengio's "Moving from System 1 DL to System 2 DL" and the concept of "(meta)-...
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1answer
43 views

What is the relationship between the Q and V functions?

Suppose we have a policy $\pi$ and we use SARSA to evaluate $Q^\pi(s, a)$, where $a$ is the policy $\pi$. Can we say that $Q^\pi(s, a) = V^\pi(s)$? The reason why I think this can be the case is ...
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1answer
50 views

What is the difference between evolutionary computation and evolutionary algorithms?

A book on evolutionary computation by De Jong mentions both the term evolutionary algorithms (EA) as well as evolutionary computation (EC). However, it remains unclear to me what the difference ...
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17 views

What are the advantages and disadvantages of extrinsic and perplexity model evaluation in NLP?

In the video Evaluation and Perplexity by Dan Jurafsky, the author talks about extrinsic and perplexity evaluation in the context of natural language processing (NLP). What are the advantages and ...
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1answer
36 views

How can the policy iteration algorithm be model-free if it uses the transition probabilities?

I'm actually trying to understand the policy iteration in the context of RL. I read an article presenting it and, at some point, a pseudo-code of the algorithm is given : What I can't understand is ...
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0answers
39 views

What's the intuition behind contrastive learning?

Recently, I have seen a surge of papers w.r.t contrastive learning (a subset of semi-supervised learning). Can anyone give a detailed explanation of this approach with its advantages/disadvantages ...
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0answers
22 views

What are the pros and cons of the common activation functions?

I have heard that sigmoid activation functions should not be used on neural networks with many hidden layers as the gradients tend to vanish in deep networks. When should each of the common ...
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3answers
192 views

What are the differences between transfer learning and meta learning?

What are the differences between meta-learning and transfer learning? I have read 2 articles on Quora and TowardDataScience. Meta learning is a part of machine learning theory in which some ...
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1answer
170 views

What’s the difference between LSTM and RNN?

What's the difference between LSTM and RNN? I know that RNN is a network layer used in neural networks, but what exactly is an LSTM? Is it also a network layer with the same characteristics?
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23 views

What is the difference between an generalised estimating equation and a recurrent neural network?

What is the difference between a generalised estimating equation (GEE) model and a recurrent neural network (RNN) model, in terms of what these two models are doing? Apart from the differences in the ...
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0answers
33 views

What are the pros and cons of Keras, PyTorch and Caffe for computer vision?

I have tried to get the basic grasp of the following deep learning frameworks with python: Keras Pytorch Caffe However, I have lately noticed that people in the computer vision community care less ...
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1answer
25 views

What is the difference between TensorFlow's callbacks and early stopping?

What is the difference between TensorFlow's callbacks and early stopping?
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1answer
43 views

Is the minimax algorithm model-based?

Trying to get my head around model-free and model-based algorithms in RL. In my research, I've seen the search trees created via the minimax algorithm. I presume these trees can only be created with a ...
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1answer
103 views

Why is the state-action value function used more than the state value function?

In reinforcement learning, the state-action value function seems to be used more than the state value function. Why is it so?
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1answer
96 views

Is there a reason to use TensorFlow over PyTorch for research purposes?

I've been using PyTorch to do research for a while and it seems to be quite easy to implement new things with. Also, it is easy to learn and I didn't have any problem with following other researchers ...
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1answer
58 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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1answer
51 views

Is there a reason to choose regular momentum over Nesterov momentum for neural networks?

I've been reading about Nesterov momentum from here and it seems like a nice improvement over regular momentum with no extra cost whatsoever. However, is this really the case? Are there instances ...
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2answers
95 views

Are PAC learnability and the No Free Lunch theorem contradictory?

I am reading the Understanding Machine Learning book by Shalev-Shwartz and Ben-David and based on the definitions of PAC learnability and No Free Lunch Theorem, and my understanding of them it seems ...
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0answers
18 views

What is the difference between graph semi-supervised learning and normal semi-supervised learning?

Whenever I look for papers involving semi-supervised learning, I always find papers that talk about graph semi-supervised learning. Could someone explain the difference between the two?
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1answer
303 views

Are model-free and off-policy algorithms the same?

In respect of RL, is model-free and off-policy the same thing, just different terminology? If not, what are the differences? I've read that the policy can be thought of as 'the brain', or decision ...
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2answers
60 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 ...
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0answers
26 views

What is the difference between using a backbone architecture and transfer learning?

I'm super new to deep learning and computer vision, so this question may sound dumb. In this link (https://github.com/GeorgeSeif/Semantic-Segmentation-Suite), there are pre-trained models (e.g., ...
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1answer
57 views

What are advantages of using meta-heuristic algorithms on optimization problems?

What are the advantages and disadvantages of using meta-heuristic algorithms on optimization problems? Simply, why do we use meta-heuristic algorithms, like PSO, over traditional mathematical ...
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1answer
29 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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2answers
53 views

Why there's so much search for Python professionals in AI? [closed]

Even with ample knowledge about the low computational performance that Python has, and even though it is an interpreted language that is not recommended for real-time applications, it is the ...
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1answer
37 views

Do we need an explicit policy to sample $A'$ in order to compute the target in SARSA or Q-learning?

I would much appreciate if you could point me in the right direction regarding this question about targets for SARSA and Q-learning (notation: $S$ is the current state, $A$ is the current action, $R$ ...
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1answer
70 views

What is the intuition behind TD($\lambda$)?

I'd like to better understand temporal-difference learning. In particular, I'm wondering if it is prudent to think about TD($\lambda$) as a type of "truncated" Monte Carlo learning?
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1answer
234 views

What is the difference between the epsilon greedy and softmax policies?

Could someone explain to me which is the key difference between the epsilon greedy policy and the softmax policy? In particular in the contest of SARSA and Q-Learning algorithms. I understood the main ...
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0answers
22 views

Can the importance sampling estimator have a non-stationary behaviour policy even if the target policy is stationary?

The inverse propensity score (IPS) estimator, which is used for off-policy evaluation in a contextual bandit problem, is well explained in the paper Doubly Robust Policy Evaluation and Optimization. ...
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1answer
149 views

In which cases is the categorical cross-entropy better than the mean squared error?

In my code, I usually use the mean squared error (MSE), but the TensorFlow tutorials always use the categorical cross-entropy (CCE). Is the CCE loss function better than MSE? Or is it better only in ...
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1answer
80 views

What are the differences between TensorFlow and PyTorch?

What are the differences between TensorFlow and PyTorch, both in terms of performance and functionality?
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0answers
35 views

How can I compare EEG data with accelerometer data in 1 algorithm?

I have frequency EEG data from fall and non-fall events and I am trying to incorporate it with accelerometer data that was collected at the same time. One approach is, of course, to use two separate ...
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3answers
106 views

Why are traditional ML models still used over deep neural networks?

I'm still on my first steps in the Data Science field. I played with some DL frameworks, like TensorFlow (pure) and Keras (on top) before, and know a little bit of some "classic machine learning" ...
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0answers
22 views

What's the difference between semi-supervised VAEs and conditional VAEs?

Can someone explain the difference? I'm assuming the difference is just that the neural nets representing the encoder and decoder are trained in a semi-supervised manner in semi-supervised VAE, which ...
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1answer
77 views

What is the difference between Sutton's and Levine's REINFORCE algorithm?

I followed the videos/slides of Berkley RL course, but now I am a bit confused when implementing it. Please see the picture below. In particular, what does $i$ represent in the REINFORCE algorithm? ...
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2answers
331 views

Is word embedding a form of feature extraction?

Feature extraction is a concept concerning the translation of raw data into the inputs that a particular machine learning algorithm requires. These derived features from the raw data that are actually ...
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1answer
65 views

What is the difference between linear and non-linear regression?

In machine learning, I understand that linear regression assumes that parameters or weights in equation should be linear. For Example: $$y = w_1x_1 + w_2x_2$$ is a linear equation where $x_1$ and $...
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26 views

What is the difference between principal component analysis and singular value decomposition in image processing?

What is the difference between principal component analysis and singular value decomposition in image processing? Which one performs better, and why?
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1answer
74 views

What are the characteristics of a deep learning AI?

I have experience in making several Artificial Neural Networks and some programs which may be classified as an Artificial Intelligence by using Tensorflow.js and Brain.js. In order to produce ...

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