Most Popular
1500 questions
5
votes
1 answer
Are these visualisations the filters of the convolution layer or the convolved images with the filters?
There are several images related to convolutional networks on the Internet, an example of which I have given below
My question is: are these images the weights/filters of the convolution layer (the weights that are learned in the learning process),…
Ali.kavari76
- 121
- 6
5
votes
1 answer
Using Reinforcement Learning in Immersive Virtual Reality to make a person move to a specific location in a virtual environment
I'm here to ask you for a solution on this problem which is: how to use Reinforcement Learning in Immersive Virtual Reality to make a person move to a specific location in a virtual environment.
Reinforcement Learning is a sub-area of Machine…
Tayyebi
- 159
- 3
5
votes
1 answer
In the field of Deep Learning research, what considerations do researchers take into account when inventing new neural network models?
I am not a researcher, but I am curious to know what considerations are relevant to take into account during research for the invention of a new neural network model, and what relevant knowledge researchers typically possess in the area.
And an…
Andres Sarmiento
- 51
- 1
5
votes
2 answers
Is an embedding a representation of a word or its meaning?
What does the term "embedding" actually mean?
An embedding is a vector, but is that vector a representation of a word or its meaning? Literature loosely uses the word for both purposes. Which one is actually correct?
Or is there anything like: A…
hanugm
- 4,102
- 3
- 29
- 63
5
votes
0 answers
Optimal episode length in reinforcement learning
I have a custom environment for stock trading where an episode can be as long as 2000-3000 steps. I've run several experiments with td3 and sac algorithms, average reward per episode flattens after few episodes. I believe average reward per episode…
Mika
- 371
- 2
- 10
5
votes
1 answer
Where do the feature extraction and representation learning differ?
Feature selection is a process of selecting a subset of features that contribute the most.
Feature extraction allows getting new features that are not actually present in the given set of features.
Representation learning is the process of learning…
hanugm
- 4,102
- 3
- 29
- 63
5
votes
2 answers
What does Brooks mean by "representation"?
For a class, I'm reading Brooks' "Intelligence without representation". The introduction is dedicated to slating representation as a focus for AI development.
I've read that representation is the problem of representing information symbolically, in…
Jansky
- 153
- 2
5
votes
2 answers
Why can't cognitive architectures achieve general intelligence?
Newbie here.
I recently read about cognitive architectures (see: https://en.wikipedia.org/wiki/Cognitive_architecture). They are supposed to be modeled after the human mind and represent a promising approach towards artificial general intelligence…
Anonymous
- 69
- 1
5
votes
1 answer
Are neurons instantly feed forward when input arrives?
Let's say I have a neural network with 5 layers, including the input and output layer. Each layer has 5 nodes. Assume the layers are fully connected, but the 3rd node in the 2nd layer is connected to the 5th node in the 4th layer. All these numbers…
Miemels
- 389
- 2
- 12
5
votes
1 answer
Why object detection algorithms are poor in optical character recognition?
OCR is still a very hard problem. We don't have universal powerful solutions. We use the CTC loss function
An Intuitive Explanation of Connectionist Temporal Classification | Towards Data Science
Sequence Modeling
With CTC | Distill
which is very…
user40943
5
votes
1 answer
What does "statistical efficiency" mean in this context?
Consider the following statement(s) from Deep Learning book (p. 333, chapter 9: Convolutional Networks) by Ian Goodfellow et al.
Convolution is thus dramatically more efficient than dense matrix
multiplication in terms of the memory requirements…
hanugm
- 4,102
- 3
- 29
- 63
5
votes
1 answer
Does the policy iteration convergence hold for finite-horizon MDP?
Most RL books (Sutton & Barto, Bertsekas, etc.) talk about policy iteration for infinite-horizon MDPs. Does the policy iteration convergence hold for finite-horizon MDP? If yes, how can we derive the algorithm?
user529295
- 379
- 2
- 12
5
votes
3 answers
Does gradient descent in deep learning assume a smooth fitness landscape?
I've come across the concept of fitness landscape before and, in my understanding, a smooth fitness landscape is one where the algorithm can converge on the global optimum through incremental movements or iterations across the landscape.
My question…
Joebevo
- 159
- 5
5
votes
3 answers
Why do we discount the state distribution?
In Reinforcement Learning, it is common to use a discount factor $\gamma$ to give less importance to future rewards when calculating the returns.
I have also seen mention of discounted state distributions. It is mentioned on page 199 of the Sutton…
David
- 5,100
- 1
- 11
- 33
5
votes
1 answer
What is the relation between self-taught learning and transfer learning?
I am new to transfer learning and I start by reading A Survey on Transfer Learning, and it stated the following:
according to different situations of labeled and unlabeled data in the source domain, we can
further categorize the inductive transfer…
Kais Hasan
- 421
- 3
- 15