Fetching the paper…
Reading the bibliography…
The rising adoption of machine learning in high energy physics and lattice field theory necessitates the re-evaluation of common methods that are widely used in computer vision, which, when applied to problems in physics, can lead to significant drawbacks in terms of performance and generalizability.
T. S. Cohen, M. Weiler, B. Kicanaoglu, M. Welling, Gauge equivariant convolutional networks and the icosahedral CNN, in: Proceedings of the 36th International Conference on Machine Learning, Vol. 97, JMLR, 2019, pp. 1321–1330 (Jun 2019) · 1902
Earlier work this paper cites.
S. J. Reddi, S. Kale, S. Kumar, On the convergence of Adam and beyond, in: International Conference on Learning Representations (ICLR), 2018 (May 2018) · 1904
Earlier work this paper cites.
T. Akiba, S. Sano, T. Yanase, T. Ohta, M. Koyama, Optuna: A next-generation hyperparameter optimization framework, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, 2019, p. 2623–2631 (Jul 2019) · 1907
Earlier work this paper cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, S. Chintala, PyTorch: An imperative style, high-performance deep learning library, in: Advances in Neural Information Processing Systems (NeurIPS), Vol. 32, 2019, pp. 8026–8037 (Dec 2019) · 1912
Earlier work this paper cites.
E. Noether, Invariante Variationsprobleme , Nachrichten von der Gesellschaft der Wissenschaften zu Göttingen, Mathematisch-Physikalische Klasse 1918 (1918) 235–257 (1918). URL http://eudml.org/doc/59024
1918
Earlier work this paper cites.
doi:10.1007/BF00342633
K. Fukushima, Cognitron: A self-organizing multilayered neural network, Biological Cybernetics 20 (3-4) (1975) 121–136 (Sep 1975) · 1975
Earlier work this paper cites.
doi:10.1007/BF00344251
K. Fukushima, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics 36 (4) (1980) 193–202 (Apr 1980) · 1980
Earlier work this paper cites.
doi:10.1007/BF02551274
G. Cybenko, Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals and Systems 2 (4) (1989) 303–314 (Dec 1989) · 1989
Earlier work this paper cites.
doi:10.1109/5.726791
Y. Lecun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE 86 (11) (1998) 2278 – 2324 (Dec 1998) · 1998
Earlier work this paper cites.
arXiv:cond-mat/0103146
N. Prokof’ev, B. Svistunov, Worm algorithms for classical statistical models, Physical Review Letters 87 (Sep 2001) · 2001
Earlier work this paper cites.
G. Kanwar, M. S. Albergo, D. Boyda, K. Cranmer, D. C. Hackett, S. Racanière, D. J. Rezende, P. E. Shanahan, Equivariant flow-based sampling for lattice gauge theory, Physical Review Letters 125 (12) (Sep 2020) · 2003
Earlier work this paper cites.
S. Blücher, L. Kades, J. M. Pawlowski, N. Strodthoff, J. M. Urban, Towards novel insights in lattice field theory with explainable machine learning, Physical Review D 101 (May 2020) · 2003
Earlier work this paper cites.
arXiv:hep-ph/0307089
T. D. Cohen, Functional integrals for QCD at nonzero chemical potential and zero density, Physical Review Letters 91 (Nov 2003) · 2003
Earlier work this paper cites.
G. Gao, J. Gao, Q. Liu, Q. Wang, Y. Wang, CNN-based density estimation and crowd counting: A survey (Mar 2020) · 2003
Earlier work this paper cites.
D. Bachtis, G. Aarts, B. Lucini, Extending machine learning classification capabilities with histogram reweighting, Physical Review E 102 (Sep 2020) · 2004
Earlier work this paper cites.
S. Pang, A. Du, M. A. Orgun, Y. Wang, Q. Sheng, S. Wang, X. Huang, Z. Yu, Beyond CNNs: Exploiting further inherent symmetries in medical images for segmentation (May 2020) · 2005
Cited alongside, same era.
K. A. Nicoli, C. J. Anders, L. Funcke, T. Hartung, K. Jansen, P. Kessel, S. Nakajima, P. Stornati, Estimation of thermodynamic observables in lattice field theories with deep generative models, Phys. Rev. Lett. 126 (2021) 032001 (Jan 2021) · 2007
Cited alongside, same era.
D. Bachtis, G. Aarts, B. Lucini, Mapping distinct phase transitions to a neural network, Physical Review E 102 (Nov 2020) · 2007
Cited alongside, same era.
D. Boyda, G. Kanwar, S. Racanière, D. J. Rezende, M. S. Albergo, K. Cranmer, D. C. Hackett, P. E. Shanahan, Sampling using S U ( N ) SU(N) gauge equivariant flows, Phys. Rev. D 103 (2021) 074504 (Apr 2021) · 2008
Cited alongside, same era.
T. S. Cohen, M. Welling, Steerable CNNs, in: International Conference on Learning Representations (ICLR), 2017 (Apr 2017) · 2017
Later among the works it cites.
D. E. Worrall, S. J. Garbin, D. Turmukhambetov, G. J. Brostow, Harmonic networks: Deep translation and rotation equivariance, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 7168–7177 (Jul 2017) · 2017
Later among the works it cites.
S. J. Wetzel, M. Scherzer, Machine learning of explicit order parameters: From the Ising model to SU(2) lattice gauge theory, Physical Review B 96 (18) (Nov 2017) · 2017
Later among the works it cites.
Z. Lu, H. Pu, F. Wang, Z. Hu, L. Wang, The expressive power of neural networks: A view from the width, in: Proceedings of the 31st International Conference on Neural Information Processing Systems, Vol. 30 of NIPS’17, Curran Associates Inc., Red Hook, NY, USA, 2017, p. 6232–6240 (Dec 2017) · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
M. Favoni, A. Ipp, D. I. Müller, D. Schuh, Lattice gauge equivariant convolutional neural networks (Dec 2020) · 2012
Cited alongside, same era.
C. Gattringer, T. Kloiber, Lattice study of the Silver Blaze phenomenon for a charged scalar ϕ 4 \phi^{4} field, Nuclear Physics B 869 (1) (2013) 56–73 (Apr 2013) · 2012
Cited alongside, same era.
M. Lin, Q. Chen, S. Yan, Network in network (Dec 2013) · 2013
Cited alongside, same era.
A. L. Maas, A. Y. Hannun, A. Y. Ng, Rectifier nonlinearities improve neural network acoustic models , in: ICML Workshop on Deep Learning for Audio, Speech and Language Processing, 2013 (Jun 2013). URL https://sites.google.com/site/deeplearningicml2013/relu_hybrid_icml2013_final.pdf
2013
Cited alongside, same era.
C. Gattringer, T. Kloiber, Spectroscopy in finite density lattice field theory: An exploratory study in the relativistic bose gas, Physics Letters B 720 (1-3) (2013) 210–214 (Mar 2013) · 2013
Cited alongside, same era.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, L. Fei-Fei, Imagenet large scale visual recognition challenge, International Journal of Computer Vision (IJCV) 115 (3) (2015) 211–252 (Dec 2015) · 2015
Cited alongside, same era.
doi:10.1109/CVPR.2016.90
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778 (Jun 2016) · 2016
Cited alongside, same era.
T. S. Cohen, M. Welling, Group equivariant convolutional networks, in: Proceedings of The 33rd International Conference on Machine Learning, Vol. 48, JMLR, 2016, pp. 2990–2999 (Jun 2016) · 2016
Cited alongside, same era.
Later among the works it cites.
D. Worrall, G. Brostow, CubeNet: Equivariance to 3D rotation and translation, in: Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 567–584 (Sep 2018) · 2018
Later among the works it cites.
B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, M. Welling, Rotation equivariant CNNs for digital pathology, in: Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2018, pp. 210–218 (Sep 2018) · 2018
Later among the works it cites.
A. S. Ecker, F. H. Sinz, E. Froudarakis, P. G. Fahey, S. A. Cadena, E. Y. Walker, E. Cobos, J. Reimer, A. S. Tolias, M. Bethge, A rotation-equivariant convolutional neural network model of primary visual cortex, in: International Conference on Learning Representations (ICLR), 2019 (May 2019) · 2019
Later among the works it cites.
K. Zhou, G. Endrődi, L.-G. Pang, H. Stöcker, Regressive and generative neural networks for scalar field theory, Physical Review D 100 (1) (Jul 2019) · 2019
Later among the works it cites.
I. Loshchilov, F. Hutter, Fixing weight decay regularization in Adam, in: International Conference on Learning Representations (ICLR), 2019 (May 2019) · 2019
Later among the works it cites.
M. W. Lafarge, E. J. Bekkers, J. P. W. Pluim, R. Duits, M. Veta, Roto-translation equivariant convolutional networks: Application to histopathology image analysis, Medical Image Analysis 68 (Feb 2021) · 2020
Later among the works it cites.
A. M. M. Scaife, F. Porter, Fanaroff-riley classification of radio galaxies using group-equivariant convolutional neural networks, Monthly Notices of the Royal Astronomical Society 503 (2) (2021) 2369–2379 (Feb 2021) · 2021
Closest in time.
K. Padavala, A. Singh, J. Kundu, Machine learned phase transitions in a system of anisotropic particles on a square lattice (Feb 2021) · 2021
Closest in time.
Y. Wang, Z. Cao, A. B. Farimani, Deep reinforcement learning optimizes graphene nanopores for efficient desalination (Jan 2021) · 2021
Closest in time.
K. Zhang, S. Lederer, K. Choo, T. Neupert, G. Carleo, E.-A. Kim, Hamiltonian reconstruction as metric for variational studies (Jan 2021) · 2021
Closest in time.