Built on

B. Hashemi, N. Amin, K. Datta, D. Olivito and M. Pierini, LHC analysis-specific datasets with Generative Adversarial Networks , 1901.05282
Original
1901
Earlier work this paper cites.
R. M. Neal, MCMC using Hamiltonian dynamics , 1206.1901
Original
1901
Earlier work this paper cites.
J. Lin, W. Bhimji and B. Nachman, Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model , JHEP 05
Original
1903
Earlier work this paper cites.
R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat and S. Palazzo, DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC , 1903.02433
Original
1903
Earlier work this paper cites.
D. Derkach, N. Kazeev, F. Ratnikov, A. Ustyuzhanin and A. Volokhova, RICH 2018 , 1903.11788
Original
1903
Earlier work this paper cites.
M. S. Albergo, G. Kanwar and P. E. Shanahan, Flow-based generative models for Markov chain Monte Carlo in lattice field theory , Phys. Rev. D100
Original
1904
Earlier work this paper cites.
E. Dupont, A. Doucet and Y. W. Teh, Augmented neural odes , 1904.01681
Original
1904
Earlier work this paper cites.
A. Butter, T. Plehn and R. Winterhalder, How to GAN LHC Events , SciPost Phys. 7
Original
1907
Earlier work this paper cites.
A. Andreassen and B. Nachman, Neural Networks for Full Phase-space Reweighting and Parameter Tuning , Phys. Rev. D 101
Original
1907
Earlier work this paper cites.
A. Andreassen and B. Nachman, Neural Networks for Full Phase-space Reweighting and Parameter Tuning , Phys. Rev. D 101
Original
1907
Earlier work this paper cites.
S. Carrazza and F. A. Dreyer, Lund jet images from generative and cycle-consistent adversarial networks , Eur. Phys. J. C79
Original
1909
Earlier work this paper cites.
C. Fanelli and J. Pomponi, DeepRICH: Learning Deeply Cherenkov Detectors , Mach. Learn. Sci. Tech. 1
Original
1911
Earlier work this paper cites.
A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman and J. Thaler, OmniFold: A Method to Simultaneously Unfold All Observables , Phys. Rev. Lett. 124
Original
1911
Earlier work this paper cites.
A. Butter, T. Plehn and R. Winterhalder, How to GAN Event Subtraction , 1912.08824
Original
1912
Earlier work this paper cites.
J. Arjona Martinez, T. Q. Nguyen, M. Pierini, M. Spiropulu and J.-R. Vlimant, Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description , ACAT 2019 (2019) , [ 1912.02748 ]
Original
1912
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn and R. Winterhalder, How to GAN away Detector Effects , 1912.00477
Original
1912
Earlier work this paper cites.
D. Belayneh et al., Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics , 1912.06794
Original
1912
Earlier work this paper cites.
Y. Wu, J. Donahue, D. Balduzzi, K. Simonyan and T. Lillicrap, Logan: Latent optimisation for generative adversarial networks , 1912.00953
Original
1912
Earlier work this paper cites.
S. Duane, A. Kennedy, B. J. Pendleton and D. Roweth, Hybrid monte carlo , Physics Letters B 195
1987
Earlier work this paper cites.
E. Bothmann, T. Janßen, M. Knobbe, T. Schmale and S. Schumann, Exploring phase space with Neural Importance Sampling , 2001.05478
Original
2001
Earlier work this paper cites.
C. Gao, S. Höche, J. Isaacson, C. Krause and H. Schulz, Event Generation with Normalizing Flows , Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
C. Gao, J. Isaacson and C. Krause, i-flow: High-Dimensional Integration and Sampling with Normalizing Flows , 2001.05486
Original
2001
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation , Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
Springer Series in Statistics. Springer New York Inc., New York, NY, USA, 2001
T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning · 2001
Earlier work this paper cites.
Cambridge University Press, 2002
A. Hatcher, Algebraic Topology · 2002
Earlier work this paper cites.
G. Kanwar, M. S. Albergo, D. Boyda, K. Cranmer, D. C. Hackett, S. Racanière et al., Equivariant flow-based sampling for lattice gauge theory , 2003.06413
Original
2003
Earlier work this paper cites.

Similar

J. Brehmer and K. Cranmer, Flows for simultaneous manifold learning and density estimation , 2003.13913
Original
2003
Cited alongside, same era.
T. Che, R. Zhang, J. Sohl-Dickstein, H. Larochelle, L. Paull, Y. Cao et al., Your gan is secretly an energy-based model and you should use discriminator driven latent sampling , 2003.06060
Original
2003
Cited alongside, same era.
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol et al., Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed , 2005.05334
Original
2005
Cited alongside, same era.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, A. Rousselot and R. Winterhalder, Invertible Networks or Partons to Detector and Back Again , 2006.06685
Original
2006
Cited alongside, same era.

Then

L. Dinh, J. Sohl-Dickstein and S. Bengio, Density estimation using real NVP , in 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings , OpenReview.net, 2017, https://openreview.net/forum?id=HkpbnH9lx
2017
Later among the works it cites.
A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta and A. A. Bharath, Generative adversarial networks: An overview , IEEE Signal Processing Magazine 35
2018
Later among the works it cites.
M. Paganini, L. de Oliveira and B. Nachman, Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters , Phys. Rev. Lett. 120
Original
2018
Later among the works it cites.
M. Paganini, L. de Oliveira and B. Nachman, CaloGAN : Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks , Phys. Rev. D97
Original
2018
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2021

Latent Space Refinement for Deep Generative Models

Winterhalder, Ramon, Bellagente, Marco, Nachman, Benjamin

Understand

Deep generative models are becoming widely used across science and industry for a variety of purposes.

A common challenge is achieving a precise implicit or explicit representation of the data probability density.
Recent proposals have suggested using classifier weights to refine the learned density of deep generative models.
We extend this idea to all types of generative models and show how latent space refinement via iterated generative modeling can circumvent topological obstructions and improve precision.

Built on

B. Hashemi, N. Amin, K. Datta, D. Olivito and M. Pierini, LHC analysis-specific datasets with Generative Adversarial Networks , 1901.05282
Original
1901
Earlier work this paper cites.
R. M. Neal, MCMC using Hamiltonian dynamics , 1206.1901
Original
1901
Earlier work this paper cites.
J. Lin, W. Bhimji and B. Nachman, Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model , JHEP 05
Original
1903
Earlier work this paper cites.
R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat and S. Palazzo, DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC , 1903.02433
Original
1903
Earlier work this paper cites.
D. Derkach, N. Kazeev, F. Ratnikov, A. Ustyuzhanin and A. Volokhova, RICH 2018 , 1903.11788
Original
1903
Earlier work this paper cites.
M. S. Albergo, G. Kanwar and P. E. Shanahan, Flow-based generative models for Markov chain Monte Carlo in lattice field theory , Phys. Rev. D100
Original
1904
Earlier work this paper cites.
E. Dupont, A. Doucet and Y. W. Teh, Augmented neural odes , 1904.01681
Original
1904
Earlier work this paper cites.
A. Butter, T. Plehn and R. Winterhalder, How to GAN LHC Events , SciPost Phys. 7
Original
1907
Earlier work this paper cites.
A. Andreassen and B. Nachman, Neural Networks for Full Phase-space Reweighting and Parameter Tuning , Phys. Rev. D 101
Original
1907
Earlier work this paper cites.
A. Andreassen and B. Nachman, Neural Networks for Full Phase-space Reweighting and Parameter Tuning , Phys. Rev. D 101
Original
1907
Earlier work this paper cites.
S. Carrazza and F. A. Dreyer, Lund jet images from generative and cycle-consistent adversarial networks , Eur. Phys. J. C79
Original
1909
Earlier work this paper cites.
C. Fanelli and J. Pomponi, DeepRICH: Learning Deeply Cherenkov Detectors , Mach. Learn. Sci. Tech. 1
Original
1911
Earlier work this paper cites.
A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman and J. Thaler, OmniFold: A Method to Simultaneously Unfold All Observables , Phys. Rev. Lett. 124
Original
1911
Earlier work this paper cites.
A. Butter, T. Plehn and R. Winterhalder, How to GAN Event Subtraction , 1912.08824
Original
1912
Earlier work this paper cites.
J. Arjona Martinez, T. Q. Nguyen, M. Pierini, M. Spiropulu and J.-R. Vlimant, Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description , ACAT 2019 (2019) , [ 1912.02748 ]
Original
1912
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn and R. Winterhalder, How to GAN away Detector Effects , 1912.00477
Original
1912
Earlier work this paper cites.
D. Belayneh et al., Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics , 1912.06794
Original
1912
Earlier work this paper cites.
Y. Wu, J. Donahue, D. Balduzzi, K. Simonyan and T. Lillicrap, Logan: Latent optimisation for generative adversarial networks , 1912.00953
Original
1912
Earlier work this paper cites.
S. Duane, A. Kennedy, B. J. Pendleton and D. Roweth, Hybrid monte carlo , Physics Letters B 195
1987
Earlier work this paper cites.
E. Bothmann, T. Janßen, M. Knobbe, T. Schmale and S. Schumann, Exploring phase space with Neural Importance Sampling , 2001.05478
Original
2001
Earlier work this paper cites.
C. Gao, S. Höche, J. Isaacson, C. Krause and H. Schulz, Event Generation with Normalizing Flows , Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
C. Gao, J. Isaacson and C. Krause, i-flow: High-Dimensional Integration and Sampling with Normalizing Flows , 2001.05486
Original
2001
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation , Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
Springer Series in Statistics. Springer New York Inc., New York, NY, USA, 2001
T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning · 2001
Earlier work this paper cites.
Cambridge University Press, 2002
A. Hatcher, Algebraic Topology · 2002
Earlier work this paper cites.
G. Kanwar, M. S. Albergo, D. Boyda, K. Cranmer, D. C. Hackett, S. Racanière et al., Equivariant flow-based sampling for lattice gauge theory , 2003.06413
Original
2003
Earlier work this paper cites.

Similar

J. Brehmer and K. Cranmer, Flows for simultaneous manifold learning and density estimation , 2003.13913
Original
2003
Cited alongside, same era.
T. Che, R. Zhang, J. Sohl-Dickstein, H. Larochelle, L. Paull, Y. Cao et al., Your gan is secretly an energy-based model and you should use discriminator driven latent sampling , 2003.06060
Original
2003
Cited alongside, same era.
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol et al., Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed , 2005.05334
Original
2005
Cited alongside, same era.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, A. Rousselot and R. Winterhalder, Invertible Networks or Partons to Detector and Back Again , 2006.06685
Original
2006
Cited alongside, same era.

Then

L. Dinh, J. Sohl-Dickstein and S. Bengio, Density estimation using real NVP , in 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings , OpenReview.net, 2017, https://openreview.net/forum?id=HkpbnH9lx
2017
Later among the works it cites.
A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta and A. A. Bharath, Generative adversarial networks: An overview , IEEE Signal Processing Magazine 35
2018
Later among the works it cites.
M. Paganini, L. de Oliveira and B. Nachman, Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters , Phys. Rev. Lett. 120
Original
2018
Later among the works it cites.
M. Paganini, L. de Oliveira and B. Nachman, CaloGAN : Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks , Phys. Rev. D97
Original
2018
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

T. Cheng, J.-F. Arguin, J. Leissner-Martin, J. Pilette and T. Golling, Variational Autoencoders for Anomalous Jet Tagging , 2007.01850

Original

2007

Cited alongside, same era.

Y. Alanazi et al., AI-based Monte Carlo event generator for electron-proton scattering , 2008.03151

Original

2008

Cited alongside, same era.

A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman and T. Plehn, GANplifying Event Samples , 2008.06545

Original

2008

Cited alongside, same era.

S. Choi, J. Lim and H. Oh, Data-driven Estimation of Background Distribution through Neural Autoregressive Flows , 2008.03636

Original

2008

Cited alongside, same era.

S. Diefenbacher, E. Eren, G. Kasieczka, A. Korol, B. Nachman and D. Shih, DCTRGAN: Improving the Precision of Generative Models with Reweighting , Journal of Instrumentation 15

Original

2009

Cited alongside, same era.

Y. Lu, J. Collado, D. Whiteson and P. Baldi, SARM: Sparse Autoregressive Model for Scalable Generation of Sparse Images in Particle Physics , 2009.14017

Original

2009

Cited alongside, same era.

K. Dohi, Variational Autoencoders for Jet Simulation , 2009.04842

Original

2009

Cited alongside, same era.

Springer Berlin Heidelberg, 2010

L. Younes, Shapes and Diffeomorphisms · 2010

Cited alongside, same era.

V. Chekalina, E. Orlova, F. Ratnikov, D. Ulyanov, A. Ustyuzhanin and E. Zakharov, Generative Models for Fast Calorimeter Simulation.LHCb case , CHEP 2018 (2018) , [ 1812.01319 ]

Original

2018