Do wide and deep networks learn the same things uncovering how neural network representations vary with width and depth?

Do wide and deep neural networks learn the same things?

In studying the effects of depth and width on internal representations, we uncover a block structure phenomenon, and demonstrate its connection to model capacity. We also show that wide and deep models exhibit systematic output differences at class and example levels.

Do different neural networks learn the same representations?

We develop a rigorous theory based on the neuron activation subspace match model. … Experimental results suggest that, surprisingly, representations learned by the same convolutional layers of networks trained from different initializations are not as similar as prevalently expected, at least in terms of subspace match.

What is the difference between shallow and deep neural network?

The terms shallow and deep refer to the number of layers in a neural network; shallow neural networks refer to a neural network that have a small number of layers, usually regarded as having a single hidden layer, and deep neural networks refer to neural networks that have multiple hidden layers.

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What makes a neural network deep versus not deep?

A deep learning system is self-teaching, learning as it goes by filtering information through multiple hidden layers, in a similar way to humans. As you can see, the two are closely connected in that one relies on the other to function. Without neural networks, there would be no deep learning.

What is depth and width of neural network?

In a Neural Network, the depth is its number of layers including output layer but not input layer. The width is the maximum number of nodes in a layer.

When and why are deep networks better than shallow ones?

While the universal approximation property holds both for hierarchical and shallow networks, deep networks can approximate the class of compositional functions as well as shallow networks but with exponentially lower number of training parameters and sample complexity.

What are representations in neural networks?

It is located at a particular layer in the network, about to launch into a function that would have worked on the received inputs. So a representation of a neuron is the portrayal of all of its possible input → output mappings.

What is centered kernel alignment?

Introduction. We apply CKA (centered kernel alignment) to measure the similarity of the hidden representations of different neural network architectures, finding that representations in wide or deep models exhibit a characteristic structure, which we term the block structure.

What is difference between deep learning and shallow learning?

In short, while many pop-science people may point towards “Deep Learning is all about stacking different neural network layers”, its main distinguishing feature from “Shallow Learning” is that Deep Learning methods derive their own features directly from data (feature learning), while Shallow Learning relies on …

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What do you understand by deep learning list the advantages of deep learning over machine learning?

Deep learning algorithms take much less time to run tests than machine learning algorithms, whose test time increases along with the size of the data. Furthermore, machine learning does not require the same costly, high-end machines and high-performing GPUs that deep learning does.

Why deep neural networks are better?

The reason behind the boost in performance from a deeper network, is that a more complex, non-linear function can be learned. Given sufficient training data, this enables the networks to more easily discriminate between different classes.

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