All of the above statements are true about a Restricted Boltzmann Machine (RBM).

1. The Positive phase of an RBM increases the probability of training data: This is the phase where the model tries to fit the data it has been given, increasing the likelihood of the training data.

2. The Negative phase of an RBM decreases the probability of samples generated by the model: In this phase, the model generates new data and tries to decrease the likelihood of these generated samples.

3. Contrastive Divergence (CD) is used to approximate the negative phase of an RBM: CD is a faster and more efficient way to approximate the negative phase, which can be computationally expensive.

4. The objective function is to maximize the likelihood of our data being drawn from the reconstructed data distribution: The goal of the RBM is to learn a probability distribution over the input data, so this statement is also true.

5. All of the above: Since all the individual statements are true, this statement is also true.

Question

All of the above statements are true about a Restricted Boltzmann Machine (RBM).

1. The Positive phase of an RBM increases the probability of training data: This is the phase where the model tries to fit the data it has been given, increasing the likelihood of the training data.

2. The Negative phase of an RBM decreases the probability of samples generated by the model: In this phase, the model generates new data and tries to decrease the likelihood of these generated samples.

3. Contrastive Divergence (CD) is used to approximate the negative phase of an RBM: CD is a faster and more efficient way to approximate the negative phase, which can be computationally expensive.

4. The objective function is to maximize the likelihood of our data being drawn from the reconstructed data distribution: The goal of the RBM is to learn a probability distribution over the input data, so this statement is also true.

5. All of the above: Since all the individual statements are true, this statement is also true.

Knowee AI · Accepted Answer

All of the above statements are true about a Restricted Boltzmann Machine (RBM).

1. The Positive phase of an RBM increases the probability of training data: This is the phase where the model tries to fit the data it has been given, increasing the likelihood of the training data.

2. The Negative phase of an RBM decreases the probability of samples generated by the model: In this phase, the model generates new data and tries to decrease the likelihood of these generated samples.

3. Contrastive Divergence (CD) is used to approximate the negative phase of an RBM: CD is a faster and more efficient way to approximate the negative phase, which can be computationally expensive.

4. The objective function is to maximize the likelihood of our data being drawn from the reconstructed data distribution: The goal of the RBM is to learn a probability distribution over the input data, so this statement is also true.

5. All of the above: Since all the individual statements are true, this statement is also true.

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