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
71 views

What is the difference between the state transition of an MDP and an action-value?

Let's say we have MDP where we have a state transition matrix. How is this state transition different from action value in reinforcement learning? Is the state transition in MDP stochastic ...
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0answers
53 views

How are the classical MDP and the object-oriented MDP views different?

I've been reading the attached paper - which aims to model entities in the world as objects, including the learning agent itself! To say the least, the goal is to navigate through what seems like a ...
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1answer
160 views

What are the differences between 1-step SARSA and SARSA?

SARSA is on-policy, while n-step SARSA is off-policy. But when n = 1, is it like an off-policy version of SARSA? Any similarity and difference between 1-step SARSA and SARSA?
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0answers
55 views

What is the relationship between PAC learning and classic parameter estimation theorems?

What are the differences and similarities between PAC learning and classic parameter estimation theorems (e.g. consistency results when estimating parameters, e.g. with MLE)?
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1answer
2k views

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 ...
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1answer
37 views

How are the Bellman optimality equations and minimax related?

Is the philosophy between Bellman equations and minimax the same? Both the algorithms look at the full horizon and take into account potential gains (Bellman) and potential losses (minimax). ...
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1answer
216 views

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 ...
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4answers
617 views

What are the pros and cons of studying machine learning before deep learning?

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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1answer
144 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 ...
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1answer
146 views

How can neural networks approximate any continuous function but have $\mathcal{VC}$ dimension only proportional to their number of parameters?

Neural networks typically have $\mathcal{VC}$ dimension that is proportional to their number of parameters and inputs. For example, see the papers Vapnik-Chervonenkis dimension of recurrent neural ...
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1answer
60 views

What is the difference between model and data distributions?

Is there any difference between the model distribution and data distribution, or are they the same?
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1answer
65 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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0answers
76 views

What is the difference between using dense layers as opposed to convolutional layers in my networks when dealing with images?

I am thinking about developing a GAN. What is the difference between using dense layers as opposed to convolutional layers in my networks when dealing with images?
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1answer
52 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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0answers
25 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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0answers
100 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
40 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
62 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
79 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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0answers
59 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
176 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
34 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
80 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
75 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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0answers
67 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
71 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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1answer
199 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
38 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
3k 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
2k views

What’s the difference between LSTM and RNN?

What's the difference between LSTM and RNN? I know that RNN is a layer used in neural networks, but what exactly is an LSTM? Is it also a layer with the same characteristics?
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0answers
33 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
82 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
54 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
153 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
85 views

What is the difference between exhaustive nearest neighbor search and k-nearest neighbour search?

I have two lists of feature vectors calculated from pre-trained CNN for image retrieval task: Query: FV_Q and Reference FV_R. <...
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1answer
239 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
150 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
86 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 ...
4
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1answer
110 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 ...
4
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2answers
328 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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1answer
47 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 some that talk about graph semi-supervised learning (e.g. A Unified Framework for Data Poisoning Attack to Graph-based Semi-...
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1answer
596 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 ...
3
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2answers
158 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
107 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., ...
2
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1answer
105 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 ...
3
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1answer
67 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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1answer
48 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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2answers
522 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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0answers
47 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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3answers
2k 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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