LESSON 13 - Generative Neural Models Flashcards
How are Boltzmann machines an extension of Hopfield networks, and what key addition is made to the architecture?
Boltzmann machines extend Hopfield networks by introducing hidden neurons to the model, providing a more powerful architecture.
What is the cognitive perspective advantage of contrastive divergence learning, and how does it differ from passive learning?
Contrastive divergence learning is more interesting from a cognitive perspective as it stimulates active brain effects, contrasting with passive learning. It involves active engagement rather than passivity.
How does the concept of a Markov blanket relate to hidden neurons in Restricted Boltzmann Machines (RBMs)?
The Markov blanket for a hidden unit in RBMs consists of visible units. This is useful for parallel sampling, treating each hypothesis as independent during inference.
What is the goal of the energy function in Restricted Boltzmann Machines, and how does it differ from feed-forward models?
The energy function in RBMs defines the most probable configurations of the system, with the goal of discovering latent structures of input data. This differs from feed-forward models as there is no specific output; the model aims to recreate input configurations.
How is computational complexity improved in Restricted Boltzmann Machines, and what role does connectivity play?
Computational complexity is improved by restricting connectivity in RBMs, creating a bipartite graph where hidden and visible neurons are fully connected, but there are no intralayer connections. This enhances ease of inference due to conditional independence among hidden neurons.
What is the significance of contrastive divergence in training RBMs, and why is it not necessary to wait for convergence in the negative phase?
Contrastive divergence is vital for training RBMs. It involves alternating between positive and negative phases, and waiting for convergence in the negative phase is unnecessary; stopping after a few iterations is often sufficient.
In autoencoders, how is reconstruction quality assessed, and what is the key goal of the architecture?
Reconstruction quality in autoencoders is assessed by subtracting the reconstructed layer from the original, aiming to minimize the difference. The primary goal is to reconstruct the input layer accurately.
How do restricted Boltzmann machines contribute to understanding the primary visual cortex, and what role do receptive fields play?
RBMs contribute to understanding the primary visual cortex by revealing that neurons become tuned to specific features, akin to receptive fields. They suggest that the sensory cortex builds a generative model by extracting basic features from data.
What insight do RBMs provide regarding neurons in the visual cortex, and what basic features do they suggest the cortex extracts?
RBMs suggest that neurons in the visual cortex respond maximally to specific localized features, such as oriented edges. This aligns with the idea that the sensory cortex builds a generative model by extracting basic features.
How does the concept of the Bayesian brain relate to perception, and what does it imply about changing beliefs based on evidence?
The Bayesian brain concept applies to perception by framing it as unconscious statistical inference. Changing beliefs based on evidence is a fundamental aspect, emphasizing the role of Bayesian logic in understanding perception.
What is the key advantage of contrastive divergence learning, and how does it incorporate both bottom-up and top-down processes?
The key advantage of contrastive divergence learning is its ability to perform both bottom-up (stimulus to answer) and top-down (projecting what’s in the mind) processes simultaneously, providing a holistic approach to information processing.
How does the Bayesian brain concept extend to the idea of unconscious statistical inference problems?
The Bayesian brain concept extends to the idea that perception and cognition can be framed as unconscious, statistical inference problems. It involves changing beliefs based on available evidence.
What is the role of the Markov blanket in RBMs, and how does it facilitate parallel sampling?
The Markov blanket in RBMs consists of the set of neighbors for a hidden unit. It is useful for parallel sampling, allowing for the observation of values in the Markov blanket simultaneously.
What makes RBMs more advantageous than Hopfield networks in terms of computational complexity, and what is the effect of restricted connectivity?
RBMs are advantageous over Hopfield networks in terms of computational complexity because they have restricted connectivity. This limitation results in a bipartite graph, simplifying calculations by rendering hidden neurons conditionally independent.
How does the concept of latent variables in RBMs make them more powerful compared to Hopfield networks?
RBMs are more powerful than Hopfield networks due to the introduction of latent variables. These hidden neurons enhance the model’s capacity to learn and represent complex patterns in the data.
