Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes.
What is weight sharing neural network?
Weight sharing is an old-school technique for reducing the number of weights in a network that must be trained; it was leveraged by LeCunn-Net circa 1998. It is exactly what it sounds like: the reuse of weights on nodes that are close to one another in some way.
Shared weights: In CNNs, each filter is replicated across the entire visual field. These replicated units share the same parameterization (weight vector and bias) and form a feature map. This means that all the neurons in a given convolutional layer respond to the same feature within their specific response field.
What is weight sharing in RNN and why is it useful?
Weight Sharing across the time stamps thus helps in understanding the sequence as well as in applied point of view reduces the training time.
Why weights are same in RNN?
To reduce the loss, we use back propagation but unlike traditional neural nets, RNN’s share weights across multiple layers or in other words it shares weight across all the time steps. This way the gradient of error at each step is also dependent on the loss at previous steps.
Which technique is used to adjust the interconnection weights between neurons of different layers?
One main part of the algorithm is adjusting the interconnection weights. This is done using a technique termed as Gradient Descent.
The main purpose of parameter sharing is a reduction of the parameters that the model has to learn. This is the whole purpose of using a RNN. If you would learn a different network for each time step and feed the output of the first model to the second etc. you would end up with a regular feed-forward network.
When running, the stacked LSTM shares weights in each time step, i.e. stacked RNN shares weights temporally, but not spatially.
Why is an RNN recurrent neural network used for machine translation?
Why is an RNN (Recurrent Neural Network) used for machine translation, say translating English to French? It can be trained as a supervised learning problem. It is strictly more powerful than a Convolutional Neural Network (CNN).
Is RNN and LSTM same?
LSTM networks are a type of RNN that uses special units in addition to standard units. LSTM units include a ‘memory cell’ that can maintain information in memory for long periods of time. A set of gates is used to control when information enters the memory, when it’s output, and when it’s forgotten.