Siavash Golkar

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Matthew M RobertsAsia Pacific Center for Theoretical PhysicsVerified email at apctp.org
David LipshutzCenter for Computational Neuroscience, Flatiron InstituteVerified email at flatironinstitute.org
Anirvan M. SenguptaProfessor of Physics and Astronomy, Rutgers UniversityVerified email at physics.rutgers.edu
Kyunghyun ChoNew York University, GenentechVerified email at nyu.edu
Tiberiu TesileanuFlatiron Institute (CCN)Verified email at flatironinstitute.org
Dung Xuan NguyenGroup leader, Institute for Basic Science, KoreaVerified email at ibs.re.kr
Yanis BahrounResearch Fellow, CCN, Flatiron Institute, Simons FoundationVerified email at flatironinstitute.org
Miles CranmerUniversity of CambridgeVerified email at cam.ac.uk
Shirley HoFlatiron Institute, Center for Computational AstrophysicsVerified email at flatironinstitute.org
Michael KaganPanofsky Fellow, SLAC, Stanford UniversityVerified email at slac.stanford.edu
Bruno Régaldo-Saint BlancardCenter for Computational Mathematics, Flatiron InstituteVerified email at flatironinstitute.org
Ruben OhanaFlatiron Institute - Simons FoundationVerified email at flatironinstitute.org
Michael McCabePhD Student at University of Colorado BoulderVerified email at colorado.edu
Alberto BiettiFlatiron Institute, Simons FoundationVerified email at nyu.edu
Liam ParkerCenter for Computational Astrophysics, Flatiron InstituteVerified email at flatironinstitute.org
François LanusseCNRS ResearcherVerified email at cea.fr
Mariel PetteeLawrence Berkeley National LaboratoryVerified email at lbl.gov
Savdeep SethiProfessor of Physics, University of ChicagoVerified email at uchicago.edu
Alexander GenkinNeuroscience Institute, NYU Langone Medical CenterVerified email at nyumc.org
Johannes FriedrichAllen Institute for Neural DynamicsVerified email at columbia.edu

Siavash Golkar

Associate Research Scientist, Flatiron Institute

Verified email at flatironinstitute.org - Homepage

Machine Learning Artificial Intelligence Theoretical Physics


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Continual Learning via Neural Pruning S Golkar, M Kagan, K Cho NeurIPS 2019 Workshop on Real Neurons & Hidden Units, 2019	142	2019
(Non)-renormalization of the chiral vortical effect coefficient S Golkar, DT Son Journal of High Energy Physics 2015 (2), 169, 2015	88	2015
Higher-spin theory of the magnetorotons S Golkar, DX Nguyen, MM Roberts, DT Son Physical review letters 117 (21), 216403, 2016	60	2016
Global anomalies and effective field theory S Golkar, S Sethi Journal of High Energy Physics 2016 (5), 1-20, 2016	58	2016
Spectral sum rules and magneto-roton as emergent graviton in fractional quantum Hall effect S Golkar, DX Nguyen, DT Son Journal of High Energy Physics 2016 (1), 1-15, 2016	55	2016
Operator product expansion and conservation laws in non-relativistic conformal field theories S Golkar, DT Son Journal of High Energy Physics 2014 (12), 1-11, 2014	35	2014
Particle-hole symmetry and composite fermions in fractional quantum Hall states DX Nguyen, S Golkar, MM Roberts, DT Son Physical Review B 97 (19), 195314, 2018	33	2018
Effective field theory of relativistic quantum Hall systems S Golkar, MM Roberts, DT Son Journal of High Energy Physics 2014 (12), 1-10, 2014	28	2014
The Euler current and relativistic parity odd transport S Golkar, MM Roberts, DT Son Journal of High Energy Physics 2015 (4), 1-22, 2015	25*	2015
A biologically plausible neural network for multichannel canonical correlation analysis D Lipshutz, Y Bahroun, S Golkar, AM Sengupta, DB Chklovskii Neural Computation 33 (9), 2309-2352, 2021	21	2021
A simple normative network approximates local non-Hebbian learning in the cortex S Golkar, D Lipshutz, Y Bahroun, A Sengupta, D Chklovskii Advances in neural information processing systems 33, 7283-7295, 2020	19	2020
Hall viscosity, spin density, and torsion M Geracie, S Golkar, MM Roberts arXiv preprint arXiv:1410.2574, 2014	19	2014
A biologically plausible neural network for Slow Feature Analysis D Lipshutz, C Windolf, S Golkar, DB Chklovskii Advances in Neural Information Processing Systems 33, 2020	17	2020
Constrained predictive coding as a biologically plausible model of the cortical hierarchy S Golkar, T Tesileanu, Y Bahroun, A Sengupta, D Chklovskii Advances in Neural Information Processing Systems 35, 14155-14169, 2022	10	2022
Inferring the quantum density matrix with machine learning K Cranmer, S Golkar, D Pappadopulo ICML 2019 Workshop on Theoretical Physics for Deep Learning, 2019	10	2019
Conformal windows of SP (2N) and SO (N) gauge theories from topological excitations on ℝ3× S1 S Golkar Journal of High Energy Physics 2009 (11), 076, 2009	10	2009
Neural optimal feedback control with local learning rules J Friedrich, S Golkar, S Farashahi, A Genkin, A Sengupta, D Chklovskii Advances in Neural Information Processing Systems 34, 16358-16370, 2021	9	2021
xVal: A continuous number encoding for large language models S Golkar, M Pettee, M Eickenberg, A Bietti, M Cranmer, G Krawezik, ... arXiv preprint arXiv:2310.02989, 2023	8	2023
Multiple physics pretraining for physical surrogate models M McCabe, BRS Blancard, LH Parker, R Ohana, M Cranmer, A Bietti, ... arXiv preprint arXiv:2310.02994, 2023	8	2023
AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models F Lanusse, L Parker, S Golkar, M Cranmer, A Bietti, M Eickenberg, ... arXiv preprint arXiv:2310.03024, 2023	6	2023

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