Fetching the paper…
Reading the bibliography…
The great success of deep learning shows that its technology contains profound truth, and understanding its internal mechanism not only has important implications for the development of its technology and effective application in various fields, but also provides meaningful insights into the understanding of human brain mechanism.
Physics of neural networks
W Kinzel · 1990
Earlier work this paper cites.
Artificial neural network methods in quantum mechanics
IE Lagaris, A Likas, and DI Fotiadis · 1997
Earlier work this paper cites.
Artificial neural network methods in quantum mechanics
IE Lagaris, A Likas, and DI Fotiadis · 1997
Earlier work this paper cites.
Quantum artificial neural network architectures and components
Ajit Narayanan and Tammy Menneer · 2000
Earlier work this paper cites.
Why the brain is probably not a quantum computer
Max Tegmark · 2000
Earlier work this paper cites.
Quantum neural network
MV Altaisky · 2001
Earlier work this paper cites.
Learning deep architectures for ai
Yoshua Bengio et al · 2009
Earlier work this paper cites.
Learning deep architectures for ai
Yoshua Bengio et al · 2009
Earlier work this paper cites.
Continuous transformation learning of translation invariant representations
G Perry, ET Rolls, and SM Stringer · 2010
Earlier work this paper cites.
Principles of quantum mechanics
Ramamurti Shankar · 2012
Earlier work this paper cites.
Impact of deep mlp architecture on different acoustic modeling techniques for under-resourced speech recognition
David Imseng, Petr Motlicek, Philip N. Garner, and Herve Bourlard · 2013
Earlier work this paper cites.
Autonomous quantum perceptron neural network
Alaa Sagheer and Mohammed Zidan · 2013
Earlier work this paper cites.
Deep learning face representation from predicting 10,000 classes
Yi Sun, Xiaogang Wang, and Xiaoou Tang · 2014
Earlier work this paper cites.
An exact mapping between the variational renormalization group and deep learning
Pankaj Mehta and David J Schwab · 2014
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Cited alongside, same era.
Deep learning
Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio · 2016
Cited alongside, same era.
Recent trends in deep learning based natural language processing
Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria · 2017
Cited alongside, same era.
Physics-guided neural networks (pgnn): An application in lake temperature modeling
Anuj Karpatne, William Watkins, Jordan Read, and Vipin Kumar · 2017
Cited alongside, same era.
Why does deep and cheap learning work so well?
Henry W Lin, Max Tegmark, and David Rolnick · 2017
Cited alongside, same era.
Efficient representation of quantum many-body states with deep neural networks
What do we understand about convolutional networks?
Isma Hadji and Richard P Wildes · 2018
Closest in time.
Weiyang Liu, Zhen Liu, Zhiding Yu, Bo Dai, Rongmei Lin, Yisen Wang, James M Rehg, and Le Song · 2018
Closest in time.
Learned deformation stability in convolutional neural networks
Avraham Ruderman, Neil Rabinowitz, Ari S Morcos, and Daniel Zoran · 2018
Closest in time.
Equivalence of restricted boltzmann machines and tensor network states
Jing Chen, Song Cheng, Haidong Xie, Lei Wang, and Tao Xiang · 2018
Closest in time.
Theory of deep learning iii: the non-overfitting puzzle
T Poggio, K Kawaguchi, Q Liao, B Miranda, L Rosasco, X Boix, J Hidary, and HN Mhaskar · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Xun Gao and Lu-Ming Duan · 2017
Cited alongside, same era.
Solving the quantum many-body problem with artificial neural networks
Giuseppe Carleo and Matthias Troyer · 2017
Cited alongside, same era.
Deep learning with Python
Francois Chollet · 2017
Cited alongside, same era.
Quantum entanglement: from quantum states of matter to deep learning
Song Cheng, Jing Chen, and Lei Wang · 2017
Cited alongside, same era.
Quantum common causes and quantum causal models
John-Mark A Allen, Jonathan Barrett, Dominic C Horsman, Ciarán M Lee, and Robert W Spekkens · 2017
Cited alongside, same era.
Deep learning and quantum physics: A fundamental bridge
Yoav Levine, David Yakira, Nadav Cohen, and Amnon Shashua · 2017
Cited alongside, same era.
Deep learning and quantum entanglement: Fundamental connections with implications to network design
Yoav Levine, David Yakira, Nadav Cohen, and Amnon Shashua · 2017
Cited alongside, same era.
Neural network renormalization group
Shuo-Hui Li and Lei Wang · 2018
Closest in time.
Mutual information, neural networks and the renormalization group
Maciej Koch-Janusz and Zohar Ringel · 2018
Closest in time.
Large scale distributed neural network training through online distillation
Rohan Anil, Gabriel Pereyra, Alexandre Passos, Robert Ormandi, George E Dahl, and Geoffrey E Hinton · 2018
Closest in time.
Deep learning: A critical appraisal
Gary Marcus · 2018
Closest in time.
Neural-network quantum states
Giuseppe Carleo, Matthias Troyer, Giacomo Torlai, Roger Melko, Juan Carrasquilla, and Guglielmo Mazzola · 2018
Closest in time.
Quantum machine learning for electronic structure calculations
Rongxin Xia and Sabre Kais · 2018
Closest in time.
Neural-network quantum state tomography
Giacomo Torlai, Guglielmo Mazzola, Juan Carrasquilla, Matthias Troyer, Roger Melko, and Giuseppe Carleo · 2018
Closest in time.
Theoretical impediments to machine learning with seven sparks from the causal revolution
Judea Pearl · 2018
Closest in time.
A mathematical framework for superintelligent machines
Daniel J Buehrer · 2018
Closest in time.