[HTML][HTML] Extensive deep neural networks for transferring small scale learning to large scale systems
We present a physically-motivated topology of a deep neural network that can efficiently
infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily …
infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily …
Extensive deep neural networks for transferring small scale learning to large scale systems
K Mills, K Ryczko, I Luchak, A Domurad, C Beeler… - Chemical …, 2019 - cir.nii.ac.jp
抄録< p> We present a physically-motivated topology of a deep neural network that can
efficiently infer extensive parameters (such as energy, entropy, or number of particles) of …
efficiently infer extensive parameters (such as energy, entropy, or number of particles) of …
[PDF][PDF] Extensive deep neural networks for transferring small scale learning to large scale systems
K Mills, K Ryczko, I Luchak, A Domurad, C Beeler… - chemical …, 2019 - academia.edu
O ðNÞ scaling. We use a form of domain decomposition for training and inference, where
each subdomain (tile) is comprised of a non-overlapping focus region surrounded by an …
each subdomain (tile) is comprised of a non-overlapping focus region surrounded by an …
Extensive deep neural networks for transferring small scale learning to large scale systems
K Mills, K Ryczko, I Luchak, A Domurad… - arXiv preprint arXiv …, 2017 - arxiv.org
We present a physically-motivated topology of a deep neural network that can efficiently
infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily …
infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily …
Extensive deep neural networks for transferring small scale learning to large scale systems
K Mills, K Ryczko, I Luchak, A Domurad, C Beeler… - Chemical Science, 2019 - Elsevier
We present a physically-motivated topology of a deep neural network that can efficiently
infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily …
infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily …
[HTML][HTML] Extensive deep neural networks for transferring small scale learning to large scale systems
K Mills, K Ryczko, I Luchak, A Domurad… - Chemical …, 2019 - ncbi.nlm.nih.gov
We present a physically-motivated topology of a deep neural network that can efficiently
infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily …
infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily …
Extensive deep neural networks for transferring small scale learning to large scale systems.
K Mills, K Ryczko, I Luchak, A Domurad, C Beeler… - Chemical …, 2019 - europepmc.org
We present a physically-motivated topology of a deep neural network that can efficiently
infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily …
infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily …
Extensive deep neural networks for transferring small scale learning to large scale systems
K Mills, K Ryczko, I Luchak, A Domurad… - arXiv e …, 2017 - ui.adsabs.harvard.edu
We present a physically-motivated topology of a deep neural network that can efficiently
infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily …
infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily …
Extensive deep neural networks for transferring small scale learning to large scale systems
K Mills, K Ryczko, I Luchak, A Domurad… - Chemical …, 2019 - pubmed.ncbi.nlm.nih.gov
We present a physically-motivated topology of a deep neural network that can efficiently
infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily …
infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily …
Extensive deep neural networks for transferring small scale learning to large scale systems.
K Mills, K Ryczko, I Luchak, A Domurad, C Beeler… - Chemical …, 2019 - europepmc.org
We present a physically-motivated topology of a deep neural network that can efficiently
infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily …
infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily …