All Questions
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84 questions with no upvoted or accepted answers
5
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3
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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 ...
4
votes
1
answer
255
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Is there any relation between the recursive neural network and recurrent neural network?
Recurrent neural networks, abbreviated as RNNs, are widely used in deep learning literature, especially for text processing.
Are they related to recursive neural networks in any way?
I am asking for ...
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4
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0
answers
61
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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)-...
4
votes
1
answer
617
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What are the differences between Bytenet and Wavenet?
I recently read Bytenet and Wavenet and I was curious why the first model is not as popular as the second. From my understanding, Bytenet can be seen as a seq2seq model where the encoder and the ...
- 1,158
4
votes
0
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614
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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
3
votes
1
answer
634
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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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3
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0
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201
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What is the difference between fuzzy neural networks and adaptive neuro fuzzy inference systems?
I have, like you see, just a general question about the combination of fuzziness and neural networks. I understood it as follows
Fuzzy neural networks as a hybrid system: the neural network helps me ...
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3
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0
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43
views
How do reinforcement learning and collaborative learning overlap?
How do reinforcement learning and collaborative learning overlap? What are the differences and similarities between these fields?
I feel like the results I get via google do not make the distinction ...
- 297
3
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0
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133
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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 ...
user9947
3
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0
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78
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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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3
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0
answers
220
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What is the difference between Squeeze-and-excite and bottleneck modules from Mobilenet v2?
Squezee-and-excite networks introduced SE blocks, while MobileNet v2 introduced linear bottlenecks.
What is the effective difference between these two concepts?
Is it only implementation (depth-wise ...
- 101
3
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0
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366
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What are the differences between CRF and HMM?
What I know about CRF is that they are discriminative models, while HMM are generative models, but, in the inference method, both use the same algorithm, that is, the Viterbi algorithm, and forward ...
- 161
3
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0
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310
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What are the key differences between cellular neural network and convolutional neural network?
What are the key differences between cellular neural networks and convolutional neural networks in terms of working principle, implementation, potential performance, and applicability?
3
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231
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What is the relation between the definition of learnability of Vapnik and Gold and learnability of neural networks?
Gold showed that a language can be learned only if it contains a finite set of sentences.
We know that deep neural networks can implement any function. Does this contradict the Gold's result?
What ...
2
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0
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111
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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 ...
- 21
2
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0
answers
61
views
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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2
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0
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28
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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,...
- 121
2
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0
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119
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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 ...
- 121
2
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1
answer
74
views
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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2
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0
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153
views
What is the difference between Probabilistic Graphical models and Graph Neural networks?
While going over PGMs and GNNs, it seems like both leverage the graph data structure. The former has been used to represent causal associations (among other things), while the latter has a varied set ...
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2
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1
answer
173
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Closed networks vs Networks with a removed delay to predict new data
I've come across two types of neural networks to predict, both from Matlab, the closed structure and the net that removes one delay to find new data.
From Matlab's app generated scripts we see:
% ...
2
votes
1
answer
860
views
Is there any difference between "image generation" and "image synthesis"?
Generative Adversarial networks (aka GANs) are used for image generation. The phrase image synthesis is also used in literature.
I know that the phrase image generation stands for
An act of ...
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2
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0
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94
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Do the terms multi-task and multi-output refer to the same thing in the context of deep learning?
Do the terms multi-task and multi-output refer to the same thing in the context of deep learning (with neural networks)? For example, do neural networks for multi-task learning use multiple outputs?
...
- 339
2
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0
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72
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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 ...
- 635
2
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0
answers
103
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 ...
- 21
2
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0
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99
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 ...
- 1,171
2
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0
answers
132
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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2
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0
answers
89
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 ...
- 175
2
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0
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262
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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2
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0
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299
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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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2
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0
answers
48
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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2
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0
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99
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What is the difference between tracking and mapping (TAM) and localization and mapping (LAM)?
In the paper Visual SLAM algorithms: a survey from 2010 to 2016 by Takafumi Taketomi, Hideaki Uchiyama and Sei Ikeda it is mentioned
It should be noted that tracking and mapping (TAM) is used instead ...
- 151
2
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0
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122
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What is the difference between evolutionary game theory and meta-heuristics?
Here is a list of meta-heuristic algorithms
Ant colony optimization,
Ant lion optimizer,
Artificial bee colony algorithm,
Bat algorithm,
Cat swarm optimization,
Crow search algorithm,
Cuckoo ...
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2
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0
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92
views
What is the complexity of policy gradient algorithms compared to discrete action space algorithms?
I am using a policy gradient algorithm (actor-critic) for wireless networks. The policy gradient-based algorithm helps because it considers continuous action space.
But how much does a policy ...
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2
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1
answer
130
views
Are there any advantages of using rules-based approaches versus models for detecting spam?
Suppose that we have unlabeled data. That is, all we have are a collection of emails and want to determine whether any of them is spam or not. Let's say we have $1,000$ rules to determine whether a ...
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2
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0
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208
views
What is the difference between visible and hidden units in Boltzmann machines?
What is the difference between visible and hidden units in Boltzmann machines? What are their purposes?
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2
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0
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76
views
Can a CNN or MLP discover similar but untrained-on patterns?
I've been experimenting with a simple tic-tac-toe game to learn neural network programming (MLP and CNNs) with good results. I train the networks on board positions and the best moves and the network ...
- 263
1
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0
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38
views
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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0
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94
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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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1
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0
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24
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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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1
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0
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117
views
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 ...
- 11
1
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0
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63
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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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0
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81
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Is the capability of RNN more than the capability of MLP?
Consider the following excerpt paragraph taken from the section titled "Recurrent Neural Networks" of the chapter 10: Sequence Modeling: Recurrent and Recursive Nets of the textbook named ...
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0
answers
79
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Is the main difference between the logistic regression and the perceptron the activation function they use?
I went through a Stats StackExchange's post about the difference between logistic regression and perceptron, which is too long to get the key point.
I'd like to consider the question in terms of the ...
- 217
1
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0
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64
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What is the conceptual difference between convolutional neural networks and auto-encoders?
I'm familiar with Auto-Encoders and I'm about to dive into CNNs. By having a look at the most important component of a CNN, the filter:
I wonder how it is different from Auto-Encoders:
For me, it ...
- 205
1
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0
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127
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What is the difference between ERL and EA by considering it as RL?
I am currently studying as an MSCS student and my research is based on Evolutionary Algorithm as Reinforcement Learning, and I am confused about the following terms:
What is the difference between ...
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1
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0
answers
281
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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 ...
- 111
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0
answers
367
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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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1
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0
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1k
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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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1
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0
answers
99
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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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