Add to ORKGThe design of deep neural networks remains somewhat of an art rather than precise science. By tentatively adopting ergodic theory considerations on top of viewing the network as the time evolution of a dynamical system, with each layer corresponding to a temporal instance, we show that some rules of thumb, which might otherwise appear mysterious, can be attributed heuristics.Comment: 6 page
In the field of machine learning, ‘deep-learning’ has become spectacularly successful very rapidly, ...
Diversity conveys advantages in nature, yet homogeneous neurons typically comprise the layers of art...
Synaptic plasticity allows cortical circuits to learn new tasks and to adapt to changing environment...
Deep learning continues to play as a powerful state-of-art technique that has achieved extraordinary...
The human brain's synapses have remarkable activity-dependent plasticity, where the connectivity pat...
Deep learning is an undeniably hot topic, not only within both academia and industry, but also among...
Standard neural networks can approximate general nonlinear operators, represented either explicitly ...
Modern Machine learning techniques take advantage of the exponentially rising calculation power in n...
The paper characterizes classes of functions for which deep learning can be exponentially better tha...
Neural networks have dramatically increased our capacity to learn from large, high-dimensional datas...
Currently there exists rather promising new trend in machine leaning (ML) based on the relationship ...
A fascinating hypothesis is that human and animal intelligence could be explained by a few principle...
Machine learning models, particularly deep artificial neural networks (ANNs), are becoming increasin...
We develop a new formulation of deep learning based on the Mori-Zwanzig (MZ) formalism of irreversib...
Advanced deep learning architectures consist of tens of fully connected and convolutional hidden lay...
In the field of machine learning, ‘deep-learning’ has become spectacularly successful very rapidly, ...
Diversity conveys advantages in nature, yet homogeneous neurons typically comprise the layers of art...
Synaptic plasticity allows cortical circuits to learn new tasks and to adapt to changing environment...
Deep learning continues to play as a powerful state-of-art technique that has achieved extraordinary...
The human brain's synapses have remarkable activity-dependent plasticity, where the connectivity pat...
Deep learning is an undeniably hot topic, not only within both academia and industry, but also among...
Standard neural networks can approximate general nonlinear operators, represented either explicitly ...
Modern Machine learning techniques take advantage of the exponentially rising calculation power in n...
The paper characterizes classes of functions for which deep learning can be exponentially better tha...
Neural networks have dramatically increased our capacity to learn from large, high-dimensional datas...
Currently there exists rather promising new trend in machine leaning (ML) based on the relationship ...
A fascinating hypothesis is that human and animal intelligence could be explained by a few principle...
Machine learning models, particularly deep artificial neural networks (ANNs), are becoming increasin...
We develop a new formulation of deep learning based on the Mori-Zwanzig (MZ) formalism of irreversib...
Advanced deep learning architectures consist of tens of fully connected and convolutional hidden lay...
In the field of machine learning, ‘deep-learning’ has become spectacularly successful very rapidly, ...
Diversity conveys advantages in nature, yet homogeneous neurons typically comprise the layers of art...
Synaptic plasticity allows cortical circuits to learn new tasks and to adapt to changing environment...