LESSON 14 - Unsupervised deep learning Flashcards
What is the stochastic nature of RBMs, and how does the activation value of a neuron relate to its likelihood of turning on?
RBMs are stochastic because activation levels represent probabilities. The activation value of a neuron indicates how likely it is to turn on.
Describe the process of learning in RBMs, including the bottom-up and top-down phases during training.
In RBMs, learning involves processing input patterns in a bottom-up phase (from visible neurons) and then a top-down phase. The activity of visible neurons is influenced by the internal states of the model.
What is the significance of stacking multiple RBMs in the context of deep learning, and how does it contribute to hierarchy?
Stacking multiple RBMs allows for the creation of deep networks. The hidden neurons of the first RBM become visible neurons for the second, forming a hierarchy of representation from simple to more complex features.
Explain the concept of sparsity in the context of RBMs and its role in improving selectivity.
Sparsity in RBMs refers to preventing too many hidden neurons from being active for the same stimulus. This improves selectivity, making the network more focused on specific features.
How does a linear classifier on top of a deep belief network (DBN) enhance its capabilities, and what is the advantage of using semi-supervised learning?
A linear classifier on top of a DBN allows for class label representation. Semi-supervised learning, using supervision for a small part of the dataset, is advantageous for training the classifier.
Elaborate on the advantage of using a trained DBN for multiple tasks, and how does it relate to the concept of hierarchy?
A trained DBN can perform multiple tasks using a small additional read-out layer, leveraging the hierarchy of hidden layers. This allows for a more abstract representation across different tasks.
In the study’s results, what does the graph with different bars represent, and what does it reveal about the advantages of a hierarchical structure?
The graph with different bars represents the resilience to noise in decoding tasks. The hierarchy of hidden layers in the DBN makes representations more abstract and robust, as evidenced by the consistently high accuracy compared to a single level RBM.
How is feedback implemented in a DBN, and what are the three main ways it can be utilized, as mentioned in the text?
Feedback in a DBN is implemented through bidirectional connections. The three main ways feedback can be utilized are generating prototypes, cross-model interactions, and mixing to solve ambiguities.
Explain the first way feedback is utilized in DBNs, known as the “generation of prototypes.”
In the “generation of prototypes,” the DBN is activated with a class label, and the generation starts from the top-hidden layer, progressing to the visible layer. This results in a prototype image representing the specified class.
How can cross-model interactions be achieved in a DBN, and what does it involve?
Cross-model interactions in a DBN involve processing different inputs, such as images and text, independently at first and later integrating them. The two inputs are initially processed independently before being jointly considered.
What is the purpose of mixing in DBNs to solve ambiguities, and how does it exploit the recurrent nature of the network?
Mixing in DBNs to solve ambiguities involves iteratively propagating activity up and down the network. This exploits the recurrent nature of the network to eliminate noise in the initial state, converging to low-energy states representing trained patterns.
How does the recurrent nature of DBNs contribute to handling noisy images and maintaining low-energy states?
The recurrent nature of DBNs helps in handling noisy images by repetitively changing activations until the network finds a stable, low-energy state. This eliminates noise and aligns with learned representations.
What application is mentioned for generating images using DBNs, and what term is used to describe this process?
DBNs can be used for generating images, particularly in the context of art creation. The term “prototypes” is used to describe the generated images.
In the context of representation learning, how is DBN utilized to build hierarchical models?
DBNs are used for representation learning, contributing to the construction of hierarchical models by capturing hierarchical features and representations in their hidden layers.
What is the fundamental difference between the bottom-up and top-down phases of learning in RBMs?
The bottom-up phase involves processing input patterns from visible neurons, while the top-down phase sees the activity of visible neurons influenced by the internal states of the model.
