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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2answers
59 views

Is there any situation in which breadth-first search is preferable over A*?

Is there any situation in which breadth-first search is preferable over A*?
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1answer
54 views

What is the difference between out of distribution detection and anomaly detection?

I'm currently reading the paper Likelihood Ratios for Out-of-Distribution Detection, and it seems that their problem is very similar to the problem of anomaly detection. More precisely, given a neural ...
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2answers
52 views

Do AlphaZero/MuZero learn faster in terms of number of games played than humans?

I don't know much about AI and am just curious. From what I read, AlphaZero/MuZero outperform any human chess player after a few hours of training. I have no idea how many chess games a very talented ...
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0answers
11 views

AI generator for imaginary street maps?

Similar to This person does not exist or This artwork does not exist, how might I go about creating a This street map does not exist, including choosing an appropriate AI model and scoping features? ...
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823 views

What is the difference between Q-learning, Deep Q-learning and Deep Q-network?

Q-learning uses a table to store all state-action pairs. Q-learning is a model-free RL algorithm, so how could there be the one called Deep Q-learning, as deep means using DNN; or maybe the state-...
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1answer
60 views

Can AI be understood as a generalized statistics tool? [duplicate]

I am a (soon-to-become, to be honest) theoretical physicist. I want to learn a bit about AI. So as you know in physics we develop theories based on as few and as simple basic equations as possible ...
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1answer
86 views

What are the advantages of RL with actor-critic methods over actor-only methods?

In general, what are the advantages of RL with actor-critic methods over actor-only (or policy-based) methods? This is not a comparison with the Q-learning series, but probably a method of learning ...
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0answers
31 views

How and why do state-of-the-art models in medical segmentation differ from general segmentation models?

I am just getting into medical image segmentation and have been able to understand the state-of-the-art architectures, like Double UNet, UNet++, and Multiresunet. What I haven't understood yet: Why ...
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1answer
161 views

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 ...
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0answers
34 views

How is few-shot learning different from transfer learning?

To my understanding, transfer learning helps to incorporate data from other related datasets and achieve the task with less labelled data (maybe in 100s of images per category). Few-shot learning ...
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1answer
86 views

What are the fundamental differences between VAE and GAN for image generation?

Starting from my own understanding, and scoped to the purpose of image generation, I'm well aware of the major architectural differences: A GAN's generator samples from a relatively low dimensional ...
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0answers
36 views

What are the pros and cons of 3D CNN and 2D CNN combined with optical flow for action recognition?

For action recognition or similar tasks, one can either use 3D CNN or combine 2D CNN with optical flow. See this paper for details. Can someone tell the pros/cons of each, in terms of accuracy, cost ...
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461 views

What is the fundamental difference between an ML model and a function?

A model can be roughly defined as any design that is able to solve an ML task. Examples of models are the neural network, decision tree, Markov network, etc. A function can be defined as a set of ...
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0answers
59 views

Are Graph Neural Networks generalizations of Convolutional Neural Networks?

In lecture 4 of this course, the instructor argues that GNNs are generalizations of CNNs, and that one can recover CNNs from GNNs. He presents the following diagram (on the right) and mentions that it ...
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1answer
43 views

What is “Pattern Theory”?

I came across Grenander's work "Probabilities on Algebraic Structures" recently and found that much of Grenander's work focused on what he called "Pattern Theory." He's written ...
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1answer
55 views

What's the difference between architectures and backbones?

In the paper "ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery", the authors talk about using: Feature Pyramid Networks (as the ...
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1answer
127 views

What is the difference between the heuristic function and the evaluation function in A*?

I am reading college notes on state search space. The notes (which are not publicly available) say: To do state-search space, the strategy involves two parts: defining a heuristic function, and ...
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1answer
79 views

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_{...
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1answer
41 views

What is the difference between object tracking and trajectory prediction?

In autonomous driving, we know that the behaviour prediction module is concerned with understanding how the agents in the environment will behave. Similarly, in the perception module, the tracking ...
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1answer
82 views

How can we derive a Convolution Neural Network from a more generic Graph Neural Network?

Convolution Neural Network (CNNs) operate over strict grid-like structures ($M \times N \times C$ images), whereas Graph Neural Networks (GNNs) can operate over all-flexible graphs, with an undefined ...
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1answer
40 views

What are the conceptual differences between regularisation and optimisation in deep neural nets?

I'm trying to wrap my mind around the concepts of regularisation and optimisation in neural nets, especially around their differences. In my current understanding, regularisation is intended to tackle ...
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1answer
92 views

Why does off-policy learning outperform on-policy learning?

I am self-studying about Reinforcement Learning using different online resources. I now have a basic understanding of how RL works. I saw this in a book: Q-learning is an off-policy learner. An off-...
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1answer
206 views

What is the difference between Stochastic Hill Climbing and Simulated Annealing?

I am reading about local search: hill climbing, and its types, and simulated annealing One of the hill climbing versions is "stochastic hill climbing", which has the following definition: ...
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0answers
39 views

How exactly is hindsight experience replay related to potential-based reward shaping?

One of the reviewers of the HER paper (which was accepted as a NIPS conference paper) wrote Overall, I'd say that it's not a huge/deep idea, but a very nice addition to the learning toolbox. When it ...
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1answer
220 views

What is the difference between the uniform-cost search and Dijkstra's algorithm?

Every computer science student (including myself, when I was doing my bachelor's in CS) probably encountered the famous single-source shortest path Dijkstra's algorithm (DA). If you also took an ...
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1answer
27 views

What is the difference between sensitivity analysis and parameter tuning?

I tried different values of genetic algorithm operators: many crossover rates from 20% to 80% many crossover rates from 1% to 20% varying the population size The study of different parameter values ...
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0answers
44 views

What is the difference between exploitation and exploration in the context of optimization?

In the paper Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm (2015, published in Knowledge-Based Systems) The test functions are divided to three groups: unimodal, multi-...
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1answer
169 views

Why are there two versions of softmax cross entropy? Which one to use in what situation?

I have seen 2 forms of softmax cross-entropy loss and are confused by the two. Which one is the right one? For example in this Quora answer, there are 2 answers: $L(\mathbf{w})=\frac{1}{N} \sum_{n=1}^...
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1answer
114 views

Is reinforcement learning only about determining the value function?

I started reading some reinforcement learning literature, and it seems to me that all approaches to solving reinforcement learning problems are about finding the value function (state-value function ...
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3answers
103 views

What is the difference between neural networks and other ways of curve fitting?

For simplicity, let's assume we want to solve a regression problem, where we have one independent variable and one dependent variable, which we want to predict. Let's also assume that there is a ...
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0answers
48 views

What is the return-to-go in reinforcement learning?

In reinforcement learning, the return is defined as some function of the rewards. For example, you can have the discounted return, where you multiply the rewards received at later time steps by ...
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0answers
23 views

What is the difference between text-based image retrieval and natural language object retrieval?

I'm working on creating a model that locates the object in the scene (2D image or 3D scene) using a natural language query. I came across this paper on natural language object retrieval, which ...
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2answers
66 views

When should one prefer using Total Variational Divergence over KL divergence in RL

In RL, both the KL divergence (DKL) and Total variational divergence (DTV) are used to measure the distance between two policies. I'm most familiar with using DKL as an early stopping metric during ...
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0answers
128 views

What is the difference between derivation and entailment?

In section 7.3 of the book Artificial Intelligence: A Modern Approach (3rd edition), it's written An inference algorithm that derives only entailed sentences is called sound or truth-preserving. The ...
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1answer
62 views

What's the difference between estimation and approximation error?

I'm unable to find online, or understand from context - the difference between estimation error and approximation error in the context of machine learning (and, specifically, reinforcement learning). ...
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0answers
42 views

What are the disadvantages of actor-only methods with respect to value-based ones?

While the advantages of actor-only algorithms, the ones that search directly the policy without the use of the value function, are clear (possibility of having a continuous action space, a stochastic ...
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1answer
63 views

What is the difference between parametric and non-parametric models?

A model can be classified as parametric or non-parametric. How are models classified as parametric and non-parametric models? What is the difference between the two approaches?
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37 views

Given the same features, do logistic regression and neural networks produce the same output?

I have a binary classification problem. I have variables (features) var1, var2, var3, ..., var14. Using these variables (aka features) in a logistic regression, I get their weights. If I use the same ...
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1answer
104 views

Why are neural networks preferred to other classification functions optimized by gradient decent

Consider a neural network, e.g. as presented by Nielsen here. Abstractly, we just construct some function $f: \mathbb{R}^n \to [0,1]^m$ for some $n,m \in \mathbb{N}$ (i.e. the dimensions of the input ...
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2answers
1k views

What is the difference between active learning and online learning?

The definitions for these two appear to be very similar, and frankly, I've been only using the term "active learning" the past couple of years. What is the actual difference between the two? ...
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1answer
167 views

How is BERT different from the original transformer architecture?

As far as I can tell, BERT is a type of Transformer architecture. What I do not understand is: How is Bert different from the original transformer architecture? What tasks are better suited for BERT,...
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0answers
56 views

What's the difference between domain randomization and domain adaptation?

In my understanding, domain randomization is one method of diversifying the dataset to achieve a better shot at domain adaptation. Am I wrong?
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1answer
72 views

Can you convert a MDP problem to a Contextual Multi-Arm Bandits problem?

I'm trying to get a better understanding of Multi-Arm Bandits, Contextual Multi-Arm Bandits and Markov Decision Process. Basically, Multi-Arm Bandits is a special case of Contextual Multi-Arm Bandits ...
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1answer
1k views

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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2answers
91 views

Why are policy iteration and value iteration studied as separate algorithms?

In Sutton and Barto's book about reinforcement learning, policy iteration and value iterations are presented as separate/different algorithms. This is very confusing because policy iteration includes ...
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1answer
306 views

What are the pros and cons of sparse and dense rewards in reinforcement learning?

From what I understand, if the rewards are sparse the agent will have to explore more to get rewards and learn the optimal policy, whereas if the rewards are dense in time, the agent is quickly guided ...
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0answers
22 views

When past states contain useful information, does A3C perform better than TD3, given that TD3 does not use an LSTM?

I am trying to build an AI that needs to have some information about the past states as well. Therefore, LSTMs are suitable for this. Now, I want to know that for a problem/game like Breakout, where ...
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1answer
71 views

What are some applications where tree models perform better than neural networks?

Neural networks are known to be generally better modeling techniques as compared to tree-based models (such as decision trees). Are there any exceptions to this?
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1answer
191 views

What exactly is the advantage of double DQN over DQN?

I started looking into the double DQN (DDQN). Apparently, the difference between DDQN and DQN is that in DDQN we use the main value network for action selection and the target network for outputting ...
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1answer
106 views

What is eager learning and lazy learning?

What is the difference between eager learning and lazy learning? How does eager learning or lazy learning help me build a neural network system? And how can I use it for any target function?

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