Uniform Cost Search Algorithm from Russell&Norvig Artificial Intelligence Book

On page 84 of Russell&Norvig’s Artificial Intelligence Book, 3rd Ed., the algorithm for uniform cost search is given. I provided a screenshot of it here for your convenience. I am having trouble understanding the highlighted line if child.STATE is not in explored **or** frontier then Shouldn’t that be if child.STATE is not in explored **and**…

Uniform Cost Search Algorithm from Russell&Norvig Artificial Intelligence Book

On page 84 of Russell&Norvig’s Artificial Intelligence Book, 3rd Ed., the algorithm for uniform cost search is given. I provided a screenshot of it here for your convenience. I am having trouble understanding the highlighted line if child.STATE is not in explored **or** frontier then Shouldn’t that be if child.STATE is not in explored **and**…

While we split data in training and test data, why we have to pairs of each?

Why do we split the data into two parts and then split those segments into training and testing data? Like we do is split in, x_train and y_train as well. Why do we have two sets of data for each training and test data?

Does bag-of-words method improve the classification accuracy?

I’m a beginner in computer vision. I want to know which structure can get better accuracy of image classification Appreciate your answers! structure 1: SIFT feature + svm structure 2: bag-of-word feature + svm reference:https://www.mathworks.com/help/vision/ug/image-classification-with-bag-of-visual-words.html

Neural network seems to just figure out the probability of a specific result

I am really new to neural networks, so i was following along with a video series, created by ‘3blue1brown’ on youtube. I created an implementation of the network he explained in c++. I am attempting to train the network to recognize hand written characters, using the MNIST data set. What seems to be happening is,…

Why does $logp_{\theta}(x^1,…,x^N)=D_{KL}(q_{\theta}(z|x^i)||p_{\phi}(z|x^i)+\mathbb{L}(\phi,\theta;x^i)$?

Why does $logp_{\theta}(x^1,…,x^N)=D_{KL}(q_{\theta}(z|x^i)||p_{\phi}(z|x^i)+\mathbb{L}(\phi,\theta;x^i)$ Where $x^i$ are data points and $z$ are latent variables I was reading the original variation autoencoder paper and I don’t understand how the marginal is equal to the RHS equation. How does the marginal equal the KL divergence of $p$ with it’s approximate distribution plus the variational lower bound. Also just…

In the reinforcement learning is the value of terminal/goal state always zero?

Let’s assume we are in the grid world with states in a 3X3 grid numbered as 0,1,…8. If 8 is the goal state and the reward of landing in the goal state is 10 and the reward of just wandering around in the grid world is 0. Is the state-value of state 8 always 0?

Early Stopping in K-Fold Cross Validation

If I use the early-stopping method in K-Fold Cross Validation (using data validation as an evaluator), how do I determine the number of epoch in testing mode after K-Fold Cross Validation? For example, if I use 3 folds (between data train and data validation) for a model: Fold #1 : stopped at epochs 234 |…

Possible model to use to find pixel locations of objects

I want to make a model that outputs the centre pixel of objects appearing in an image. My current method involves using a CNN with L2 loss to output an image of equivalent size to the input where each pixel has a value of 1 if it is the center of an object and 0…

Architecture of the encoder in a Bi-GAN?

I know this is a subjective question, but I was thinking how does one decide on their encoder architecture in the case of Bi-directional GANs. The first idea coming to my mind is basically mirroring the generator’s architecture, which can end up being something very similar to the discriminator architecture. Nonetheless, mirroring the generator’s architecture…