Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Sébastien Racanière"'
Publikováno v:
Frontiers in Computational Neuroscience, Vol 16 (2022)
Biological intelligence is remarkable in its ability to produce complex behavior in many diverse situations through data efficient, generalizable, and transferable skill acquisition. It is believed that learning “good” sensory representations is
Externí odkaz:
https://doaj.org/article/4489b7ae0df8408382912b16fcc18d97
Autor:
Ryan Abbott, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Betsy Tian, Julian M. Urban
Publikováno v:
Physical Review
This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as stochastic estimators for the fermionic determinant. This is the default approach in state-of-the-art lattice field
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a72eadee7f320bf32462ed19c653214
https://doi.org/10.1103/physrevd.106.074506
https://doi.org/10.1103/physrevd.106.074506
Autor:
Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
Publikováno v:
Physical Review
Recent results suggest that flow-based algorithms may provide efficient sampling of field distributions for lattice field theory applications, such as studies of quantum chromodynamics and the Schwinger model. In this work, we provide a numerical dem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::89a3d9ba0aaca8a6925826025b5faaa3
http://arxiv.org/abs/2202.11712
http://arxiv.org/abs/2202.11712
Autor:
Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan
Publikováno v:
Physical Review
Algorithms based on normalizing flows are emerging as promising machine learning approaches to sampling complicated probability distributions in a way that can be made asymptotically exact. In the context of lattice field theory, proof-of-principle s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e1dc9f5c53d934d418b36d00950f3ed7
http://repo.scoap3.org/api
http://repo.scoap3.org/api
Publikováno v:
Frontiers in computational neuroscience. 16
Biological intelligence is remarkable in its ability to produce complex behavior in many diverse situations through data efficient, generalizable, and transferable skill acquisition. It is believed that learning “good” sensory representations is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7fe337b535489ee7ca82d2a3468134da
https://pubmed.ncbi.nlm.nih.gov/35493854
https://pubmed.ncbi.nlm.nih.gov/35493854
Autor:
Phiala E. Shanahan, Michael S. Albergo, Danilo Jimenez Rezende, D. L. Boyda, Gurtej Kanwar, Daniel C. Hackett, Kyle Cranmer, Sébastien Racanière
Publikováno v:
Physical Review
We develop a flow-based sampling algorithm for $SU(N)$ lattice gauge theories that is gauge-invariant by construction. Our key contribution is constructing a class of flows on an $SU(N)$ variable (or on a $U(N)$ variable by a simple alternative) that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::907ffc0f71825161ba62b085f77d6329
https://doi.org/10.1103/physrevd.103.074504
https://doi.org/10.1103/physrevd.103.074504
Autor:
Peter Wirnsberger, George Papamakarios, Borja Ibarz, Sébastien Racanière, Andrew J Ballard, Alexander Pritzel, Charles Blundell
Publikováno v:
Machine Learning: Science and Technology. 3:025009
We present a machine-learning approach, based on normalizing flows, for modelling atomic solids. Our model transforms an analytically tractable base distribution into the target solid without requiring ground-truth samples for training. We report Hel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::741f7062ece403cca27e9ad89d3a0327
https://doi.org/10.1088/2632-2153/ac6b16
https://doi.org/10.1088/2632-2153/ac6b16
Autor:
Andrew J. Ballard, Alexander Pritzel, Charles Blundell, Stuart Abercrombie, Sébastien Racanière, Peter Wirnsberger, George Papamakarios, Danilo Jimenez Rezende
Publikováno v:
The Journal of chemical physics. 153(14)
Free energy perturbation (FEP) was proposed by Zwanzig more than six decades ago as a method to estimate free energy differences, and has since inspired a huge body of related methods that use it as an integral building block. Being an importance sam
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cdd2d016280e02e396f67279c344eade
https://pubmed.ncbi.nlm.nih.gov/33086827
https://pubmed.ncbi.nlm.nih.gov/33086827
Autor:
Danilo Jimenez Rezende, D. L. Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Phiala E. Shanahan, Sébastien Racanière, Michael S. Albergo
Publikováno v:
Physical Review Letters
We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge-invariant by construction. We demonstrate the application of this framework to U(1) gauge theory in two spacetime dimensions, and find that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c05d88c21ae8c521893b58dc04a59c08
http://repo.scoap3.org/api
http://repo.scoap3.org/api
Publikováno v:
Concurrency and Computation: Practice and Experience. 24:880-894
We report new results from an on-going project to accelerate derivatives computations. Our earlier work was focused on accelerating the valuation of credit derivatives. In this paper, we extend our work in two ways: by applying the same techniques, f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e58f3a504511a5a7ce2a65f8a3ad33a9
https://doi.org/10.1002/cpe.1778
https://doi.org/10.1002/cpe.1778