The problem of computing the storage capacity of a feed-forward network, with L hidden layers, N inputs, and K units in the first hidden layer, is analyzed using techniques from statistical mechanics. We found that the storage capacity strongly depends on the network architecture αc ∼ (log K)1-1/2L and that the number of units K limits the number of possible hidden layers L through the relationship 2L - 1 < 2log K
The capacity C_b of two later (N - 2L - 1) feed-forward neural networks is shown to satisfy the rela...
An autoassociative network of Potts units, coupled via tensor connections, has been proposed and ana...
Understanding the theoretical foundations of how memories are encoded and retrieved in neural popula...
The problem of computing the storage capacity of a feed-forward network, with L hidden layers, N inp...
The storage capacity of multilayer networks with overlapping receptive fields is investigated for a ...
We obtained an analytical expression for the computational complexity of many layered committee mach...
The problem of learning by examples in ultrametric committee machines (UCMs) is studied within the f...
We propose to measure the memory capacity of a state machine by the numbers of discernible states, w...
We study a fully-connected parity machine with K hidden units for continuous weights. The geometrica...
A long standing open problem in the theory of neural networks is the development of quantitative met...
This paper presents a probabilistic approach based on collisions to assess the storage capacity of R...
Determining the memory capacity of two layer neural networks with $m$ hidden neurons and input dimen...
We study the number p of unbiased random patterns which can be stored in a neural network of N neuro...
<p><b>A,</b> Contour plot of pattern capacity (number of stored memories) as a function of assembly...
In standard attractor neural network models, specific patterns of activity are stored in the synapti...
The capacity C_b of two later (N - 2L - 1) feed-forward neural networks is shown to satisfy the rela...
An autoassociative network of Potts units, coupled via tensor connections, has been proposed and ana...
Understanding the theoretical foundations of how memories are encoded and retrieved in neural popula...
The problem of computing the storage capacity of a feed-forward network, with L hidden layers, N inp...
The storage capacity of multilayer networks with overlapping receptive fields is investigated for a ...
We obtained an analytical expression for the computational complexity of many layered committee mach...
The problem of learning by examples in ultrametric committee machines (UCMs) is studied within the f...
We propose to measure the memory capacity of a state machine by the numbers of discernible states, w...
We study a fully-connected parity machine with K hidden units for continuous weights. The geometrica...
A long standing open problem in the theory of neural networks is the development of quantitative met...
This paper presents a probabilistic approach based on collisions to assess the storage capacity of R...
Determining the memory capacity of two layer neural networks with $m$ hidden neurons and input dimen...
We study the number p of unbiased random patterns which can be stored in a neural network of N neuro...
<p><b>A,</b> Contour plot of pattern capacity (number of stored memories) as a function of assembly...
In standard attractor neural network models, specific patterns of activity are stored in the synapti...
The capacity C_b of two later (N - 2L - 1) feed-forward neural networks is shown to satisfy the rela...
An autoassociative network of Potts units, coupled via tensor connections, has been proposed and ana...
Understanding the theoretical foundations of how memories are encoded and retrieved in neural popula...