LESSON 12 - Associative memories Flashcards
What were the breakthroughs in the 80s related to neural networks, and how did they contribute to unsupervised learning?
In the 80s, breakthroughs like Hopfield networks and backpropagation allowed training networks with hidden units and carrying out supervised learning, contributing to the development of more complex unsupervised learning models.
In unsupervised learning, what is the primary goal of clustering, and what examples of clustering were discussed?
The primary goal of clustering in unsupervised learning is to group patterns with similar structures. Examples include clustering male-female faces and personality types.
How is the Hebb rule applied in associative memories, and what is the significance during recall?
The Hebb rule, involving the multiplication of the learning rate by the co-activation of two neurons, is applied during associative memories to increase connection strength during learning. During recall, it helps in associating incomplete information, such as recalling faces based on shapes.
What is the approach to storing binary patterns in neural networks, and how is noisy pattern recall handled?
Binary patterns are stored by encoding images as 1s and 0s in neurons corresponding to pixels. For noisy pattern recall, the network aims to recall the original image even with injected noise.
How did the perspective shift from deterministic to probabilistic systems influence neural networks?
The shift from deterministic to probabilistic systems considered neural networks as probabilistic entities, mirroring physical systems. This perspective led to exploring the Ising model for studying phase transitions.
Explain the frustration concept in physical systems, and why is it considered useful?
Frustration in physical systems arises when they cannot reach a completely uniform state. In neural networks and the brain, frustration is useful as it introduces entropy and diversity of signals, enabling better information encoding.
How did Hopfield extend simple physical models to stimulate neural networks, and what was the analogy drawn?
Hopfield extended simple physical models, like the Ising model, to stimulate neural networks. The analogy was drawn between physical material and neurons.
What is the architecture of a Hopfield network, and what are its limitations?
The architecture of a Hopfield network is fully recurrent with visible neurons representing pixels. It lacks hidden units, limiting its expressiveness.
What is the core of Hopfield networks, and how is the energy function defined?
The core of Hopfield networks is the energy function, defined as the weighted sum of co-activating neurons. The energy function has multiple minima, each corresponding to an attractor.
How is the dynamics of a Hopfield network defined, and what activates neurons during the process?
The dynamics involve a step function, similar to a perceptron, where neurons activate if the weighted sum exceeds a threshold. Learning involves using a Hebbian rule to set the weights.
What is the goal of learning in Hopfield networks, and what role does energy play?
The goal of learning is to shape the energy function, creating attractors for local energy minima. After learning, low energy corresponds to meaningful patterns, facilitating unsupervised learning.
How can Hopfield networks be adapted for supervised learning, and what elements can be included in the network?
Hopfield networks can be adapted for supervised learning by including visible units for pixels and elements encoding a label. This enables the network to recall patterns with corresponding labels.
What challenges arise in Hopfield networks, leading to incorrect memories, and how is stochastic dynamics introduced?
Challenges include reaching configurations with low energy but incorrect memories. Stochastic dynamics are introduced using a Sigmoid function and temperature, preventing deterministic behavior.
What is the role of temperature in making Hopfield networks stochastic, and how does it impact the behavior?
Temperature, through the Sigmoid function, influences the stochasticity of Hopfield networks. Lower temperatures result in more deterministic behavior, while higher temperatures introduce randomness.
Why is injecting randomness into the system essential in Hopfield networks, and what role does momentum play?
Injecting randomness helps prevent trapping in local minima, allowing the system to approach global minima. Momentum is introduced to facilitate jumping to global minima by adding some randomness.
