^abDynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networkshttps://proceedings.mlr.press/v80/chen18i.html ^abDynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networkshttps://procee...
Dynamical isometry and a mean field theory of RNNs: Gating en- ables signal propagation in recurrent neural networks. In International Conference on Machine Learning, 2018.M. Chen, J. Pennington, and S. Schoenholz. Dynamical isometry and a mean field theory of rnns: Gating enables signal ...
Recurrent neural networks have gained widespread use in modeling sequence data across various domains. While many successful recurrent architectures employ a notion of gating, the exact mechanism that enables such remarkable performance is not well understood. We develop a theory for signal propagation in...
Spiking neuronal networksare a type of neural network model where the neurons interact by sending and receiving the so-called spikes, short pulses that are only defined by their time of occurrence. Biologically, spikes correspond to the action potentials of neurons. Neuron models that produce spikes...
A Recurrent Neural Network (RNN) is an extension of conventional deep learning neural networks incorporating connections that feedback the hidden layers of the neural network into themselves, called recurrent connections. However, due to the gradient disappearing, exploding problems, and trouble learning...
Brain networks change along the four cycles, in precision, connectivity, and brain rhythms. Principles of mind-brain interaction are discussed. Keywords: brain; mind; development; intelligence; mental and brain changes; brain networks 1. Introduction The brain is a biophysical system collecting ...
A Recurrent Neural Network (RNN) is an extension of conventional deep learning neural networks incorporating connections that feedback the hidden layers of the neural network into themselves, called recurrent connections. However, due to the gradient disappearing, exploding problems, and trouble learning...