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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Why are these two implementations of the $\epsilon$-greedy policy different?
According to the book Reinforcement Learning An Introduction, the epsilon greedy policy can generally implemented as:
$$
\pi(a|s) =
\begin{cases}
\frac{\epsilon}{|A|} + 1 - \epsilon & \text{if } ...
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What are the similarities between Q-learning and Value Iteration?
This is the explanation of value iteration in our notes where you keep applying bellman optimality equation till it stops changing and then acting greedily wrt the value function gives the optimal ...
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Is there validation data in K-fold cross-validation?
We know that in machine learning the dataset is divided into 3 parts: training data, validation data and test data.
On the other hand, K-fold cross-validation is defined as follows:
the dataset is ...
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What is the difference between Machine Learning model, algorithm and hypothesis?
I'm fairly new to Machine Learning field and still to grasp the basics, so this question may seem very stupid, but what is the difference between Machine Learning model, algorithm and hypothesis?
Like ...
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Which search algorithm expands nodes closest to the goal?
I want to know which search algorithm among A* and Best-First Search and Greedy First Search expands nodes closest to the goal. I have three opinions about A* and Best-First Search and Greedy First ...
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What is the difference between A/B testing and Reinforcement Learning?
I was learning ML, and I learnt a new section called, Reinforcement Learning. After some research on web, I found that it is a trial and error technique by which ...
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What are the advantages of using RL over classical approaches in problems such as collision avoidance and path planning?
What are the advantages of going for RL based approaches than classical approaches (geometric for example) in problems such as collision avoidance and path planning?
Are RL based solutions better for ...
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Is there any available assessment of relative capabilities of Claude-2 and GPT-4?
My first experience with Claude-2 indicates that it has capabilities well comparable to GPT-4 and noticably above those of GPT-3.5.
There is an AI models ladder but it currently does not include ...
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How are the intuitions and mathematics of attention mechanisms related to those of PageRank?
Excuse me if you find this question too vague and not fitting to this forum and feel free to close it. The overall goal of my question is to get a better intuition of the attention concept and ...
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How does Monte-Carlo Tree Search Compare to MCMC?
Monte-Carlo Tree Search was the method used for AlphaGo my understanding is: it would randomly search the state space of possible moves where the probability of choosing a move was proportional to the ...
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What's the difference between GPT3.5 and InstructGPT?
I read about the different model series in GPT3.5 here - https://platform.openai.com/docs/models/gpt-3-5
At the beginning of the page, it mentions to look at https://platform.openai.com/docs/model-...
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Are on-policy algorithms always better than off-policy ones?
I am studying RL and I have a question: Are on-policy algorithms always better than off-policy ones?
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How are these two equations for the optimal state-value function equivalent?
By substituting the optimal policy $\pi_{\star}$ into the Bellman equation, we get the Bellman equation for $v_{\pi_{\star}}(s)=v_{\star}(s)$:
$$ v_{\star}(s) = \sum\limits_a \pi_{\star}(a|s) \sum\...
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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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What's the difference between classification and segmentation in deep learning?
What's the difference between classification and segmentation in deep learning?
In particular, can the classification loss function be used for segmentation problems?
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What is the precise relation between Swarm Intelligence and Ensemble Methods?
I come from the machine learning side of AI, and have recently become more interested in the bio-inspired side of AI. Specifically I started reading about swarm intelligence and immediately started ...
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Are autoencoders computationally cheaper than MLPs with the same number of neurons?
Are autoencoders computationally cheaper than other neural networks such as MLP with the same number of neurons?
I have read in some papers that autoencoders train the network faster, and I could ...
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What are the similarities and differences between ChatGPT and YouChat?
I have recently tried the both, and it looks to me that the both have similar capabilities.
YouChat says ChatGPT is more advanced.
YouChat has connection to the Internet, and according to it, ...
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For specific tasks, is it better to fine-tune models on examples or just use prompting with the context of the task?
These days large language models cover a vast amount of topics and information, but I wanted to understand: For specific tasks, is it better to fine-tune models on examples or just use prompting with ...
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Should I use multi-armed-bandits or RL for a financial time-series problem?
If we take simple financial timeseries data(stock/commodity/currency prices), State(t+1) does not depend on the action that we choose to take at State(t) as in Maze or Chess problem.
Simple example: ...
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Is my understanding correct regarding the difference between policy and plan?
I am confused regarding the difference between policy and plan in reinforcement learning. According to my understanding, when we calculate the value of state using Bellman equation in deterministic ...
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How do transformers compare to CNNs in terms of compute budget (and computing time) during inference?
Transformers are data and GPU hungry during training. Is this also true at inference time? How do transformers compare to feedforward CNNs e.g., during bounding box generation at inference time? I ...
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Is there an AI technique (or general programming technique) suitable for seeing if two articles deal with the same event?
I'm looking for a way to work out if two or more articles deal with the same event or issue and I'm not sure where to start.
For example, back in August 2022 there were a few articles on the latest ...
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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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Why and when do we use ReLU over tanh activation function?
I was reading LeCun Efficient Backprop and the author repeated stressed the importance of average the input patterns at 0 and thus justified the usage of tanh sigmoid. But if tanh is good then how ...
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As someone starting out in RL, could you help me understand the differences between actor-only, critic-only, and actor-critic methods?
I have been reading some medium articles and these three methods pop up a lot. I am wondering what the differences between these are, what are the advantages of one over the other, etc. Also from my ...
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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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Why does mean episode reward during training differ dramatically from "manual" runs of the trained model on same data?
I am training an RL agent, using PPO, on a time-series environment that comes from a tabular dataset. The possible scores during an episode goes from -1 to positive infinity (though realistically, I ...
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When are traditional image processing methods preferable to machine learning and why?
By traditional image processing I understand, e. g. using filters to improve the image, extracting edges and then classifying objects using template matching.
My current decision criteria are:
large ...
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What is the difference between the triplet loss and the contrastive loss?
What is the difference between the triplet loss and the contrastive loss?
They look same to me. I don't understand the nuances between the two. I have the following queries:
When to use what?
What ...
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Is item-based collaborative filtering the same thing as content-based filtering?
According to this Google dev page
content-based filtering
Uses similarity between items to recommend items similar to what the
user likes.
collaborative filtering
Uses similarities between queries ...
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Is there a way to improve the low-quality data?
I'm on a robotics team and we've been tasked to write a program to differentiate between a live and dead fish. We've been given ~15 minutes of training footage and it's absolutely terrible. It's low ...
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What are all the possible usages of 'multilayer perceptron'?
The term 'multilayer perceptron' has been used in literature in various ways in the literature.
I am presenting some of them below
As a feed-forward neural network [1].
As a fully connected feed-...
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What are the specific differences between vision transformers variants?
I have tried 4 different types of attacks on vision transformers (ViT small and tiny, DeiT small and tiny) but the attack successes on smaller versions are higher than the tiny versions. My ...
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What is the difference between features and inputs in machine learning?
I have seen many places that features and inputs have been used interchangeably when talking about machine learning especially deep neural networks. I want to know if they are indeed the same thing or ...
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What are the differences between BLEU and METEOR?
I am trying to understand the concept of evaluating the machine translation evaluation scores.
I understand how what BLEU score is trying to achieve. It looks into different n-grams like BLEU-1,BLEU-2,...
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What is the difference between a policy and rewards?
I don't understand the difference between a policy and rewards. Sure, a policy tells us what to do, but isn't the output of a neural network trained on rewards basically a policy (i.e. choose the ...
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What is the difference between CNN-LSTM and RNN?
I'm starting to study RNN for a project of video prediction, but I encounter these CNN-LSTM models. Initially, I thought that is another name for RNN, but I think I get it wrong. Since I'm a beginner ...
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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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What are the benefits of using spectral k-means over simple k-means?
I have understood why k-means can get stuck in local minima.
Now, I am curious to know how the spectral k-means helps to avoid this local minima problem.
According to this paper A tutorial on Spectral,...
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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 ...
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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 ...
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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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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 ...
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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 ...
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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 ...
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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 ...
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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....
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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 ...
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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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