1 code implementation • 11 Oct 2023 • Yi Sui, Tongzi Wu, Jesse C. Cresswell, Ga Wu, George Stein, Xiao Shi Huang, Xiaochen Zhang, Maksims Volkovs
Self-supervised representation learning~(SSRL) has advanced considerably by exploiting the transformation invariance assumption under artificially designed data augmentations.
2 code implementations • NeurIPS 2023 • George Stein, Jesse C. Cresswell, Rasa Hosseinzadeh, Yi Sui, Brendan Leigh Ross, Valentin Villecroze, Zhaoyan Liu, Anthony L. Caterini, J. Eric T. Taylor, Gabriel Loaiza-Ganem
Comparing to 17 modern metrics for evaluating the overall performance, fidelity, diversity, rarity, and memorization of generative models, we find that the state-of-the-art perceptual realism of diffusion models as judged by humans is not reflected in commonly reported metrics such as FID.
1 code implementation • 15 Jul 2022 • George Stein, Uros Seljak, Vanessa Bohm, G. Aldering, P. Antilogus, C. Aragon, S. Bailey, C. Baltay, S. Bongard, K. Boone, C. Buton, Y. Copin, S. Dixon, D. Fouchez, E. Gangler, R. Gupta, B. Hayden, W. Hillebrandt, M. Karmen, A. G. Kim, M. Kowalski, D. Kusters, P. F. Leget, F. Mondon, J. Nordin, R. Pain, E. Pecontal, R. Pereira, S. Perlmutter, K. A. Ponder, D. Rabinowitz, M. Rigault, D. Rubin, K. Runge, C. Saunders, G. Smadja, N. Suzuki, C. Tao, R. C. Thomas, M. Vincenzi
We construct a physically-parameterized probabilistic autoencoder (PAE) to learn the intrinsic diversity of type Ia supernovae (SNe Ia) from a sparse set of spectral time series.
2 code implementations • 25 Oct 2021 • George Stein, Peter Harrington, Jacqueline Blaum, Tomislav Medan, Zarija Lukic
We present the use of self-supervised learning to explore and exploit large unlabeled datasets.
1 code implementation • 30 Sep 2021 • George Stein, Jacqueline Blaum, Peter Harrington, Tomislav Medan, Zarija Lukic
We employ self-supervised representation learning to distill information from 76 million galaxy images from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys' Data Release 9.
no code implementations • 12 Jan 2021 • Md Abul Hayat, Peter Harrington, George Stein, Zarija Lukić, Mustafa Mustafa
We use a contrastive self-supervised learning framework to estimate distances to galaxies from their photometric images.
no code implementations • 5 Jan 2021 • Bruno Regaldo-Saint Blancard, Sandrine Codis, J. Richard Bond, George Stein
The anisotropy or triaxiality of massive dark matter haloes largely defines the structure of the cosmic web, in particular the filaments that join the haloes together.
Cosmology and Nongalactic Astrophysics
1 code implementation • 24 Dec 2020 • Md Abul Hayat, George Stein, Peter Harrington, Zarija Lukić, Mustafa Mustafa
We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks.
no code implementations • 21 Dec 2020 • George Stein, Uros Seljak, Biwei Dai
Anomaly detection is a key application of machine learning, but is generally focused on the detection of outlying samples in the low probability density regions of data.
2 code implementations • 6 May 2020 • Richard M. Feder, Philippe Berger, George Stein
Fast and accurate simulations of the non-linear evolution of the cosmic density field are a major component of many cosmological analyses, but the computational time and storage required to run them can be exceedingly large.
Cosmology and Nongalactic Astrophysics Computational Physics
2 code implementations • 24 May 2019 • Daniel N. Pfeffer, Patrick C. Breysse, George Stein
We evaluate the trained CNN on a number of noise scenarios in order to determine how robust the network predictions are for application to realistic data.
Cosmology and Nongalactic Astrophysics
1 code implementation • 17 Oct 2018 • George Stein, Marcelo A. Alvarez, J. Richard Bond
We present a detailed description and validation of our massively-parallel update to the mass-Peak Patch method, a fully predictive initial-space algorithm to quickly generate dark matter halo catalogues in very large cosmological volumes.
Cosmology and Nongalactic Astrophysics
1 code implementation • 22 Aug 2018 • The Simons Observatory Collaboration, Peter Ade, James Aguirre, Zeeshan Ahmed, Simone Aiola, Aamir Ali, David Alonso, Marcelo A. Alvarez, Kam Arnold, Peter Ashton, Jason Austermann, Humna Awan, Carlo Baccigalupi, Taylor Baildon, Darcy Barron, Nick Battaglia, Richard Battye, Eric Baxter, Andrew Bazarko, James A. Beall, Rachel Bean, Dominic Beck, Shawn Beckman, Benjamin Beringue, Federico Bianchini, Steven Boada, David Boettger, J. Richard Bond, Julian Borrill, Michael L. Brown, Sarah Marie Bruno, Sean Bryan, Erminia Calabrese, Victoria Calafut, Paolo Calisse, Julien Carron, Anthony Challinor, Grace Chesmore, Yuji Chinone, Jens Chluba, Hsiao-Mei Sherry Cho, Steve Choi, Gabriele Coppi, Nicholas F. Cothard, Kevin Coughlin, Devin Crichton, Kevin D. Crowley, Kevin T. Crowley, Ari Cukierman, John M. D'Ewart, Rolando Dünner, Tijmen de Haan, Mark Devlin, Simon Dicker, Joy Didier, Matt Dobbs, Bradley Dober, Cody J. Duell, Shannon Duff, Adri Duivenvoorden, Jo Dunkley, John Dusatko, Josquin Errard, Giulio Fabbian, Stephen Feeney, Simone Ferraro, Pedro Fluxà, Katherine Freese, Josef C. Frisch, Andrei Frolov, George Fuller, Brittany Fuzia, Nicholas Galitzki, Patricio A. Gallardo, Jose Tomas Galvez Ghersi, Jiansong Gao, Eric Gawiser, Martina Gerbino, Vera Gluscevic, Neil Goeckner-Wald, Joseph Golec, Sam Gordon, Megan Gralla, Daniel Green, Arpi Grigorian, John Groh, Chris Groppi, Yilun Guan, Jon E. Gudmundsson, Dongwon Han, Peter Hargrave, Masaya Hasegawa, Matthew Hasselfield, Makoto Hattori, Victor Haynes, Masashi Hazumi, Yizhou He, Erin Healy, Shawn W. Henderson, Carlos Hervias-Caimapo, Charles A. Hill, J. Colin Hill, Gene Hilton, Matt Hilton, Adam D. Hincks, Gary Hinshaw, Renée Hložek, Shirley Ho, Shuay-Pwu Patty Ho, Logan Howe, Zhiqi Huang, Johannes Hubmayr, Kevin Huffenberger, John P. Hughes, Anna Ijjas, Margaret Ikape, Kent Irwin, Andrew H. Jaffe, Bhuvnesh Jain, Oliver Jeong, Daisuke Kaneko, Ethan D. Karpel, Nobuhiko Katayama, Brian Keating, Sarah S. Kernasovskiy, Reijo Keskitalo, Theodore Kisner, Kenji Kiuchi, Jeff Klein, Kenda Knowles, Brian Koopman, Arthur Kosowsky, Nicoletta Krachmalnicoff, Stephen E. Kuenstner, Chao-Lin Kuo, Akito Kusaka, Jacob Lashner, Adrian Lee, Eunseong Lee, David Leon, Jason S. -Y. Leung, Antony Lewis, Yaqiong Li, Zack Li, Michele Limon, Eric Linder, Carlos Lopez-Caraballo, Thibaut Louis, Lindsay Lowry, Marius Lungu, Mathew Madhavacheril, Daisy Mak, Felipe Maldonado, Hamdi Mani, Ben Mates, Frederick Matsuda, Loïc Maurin, Phil Mauskopf, Andrew May, Nialh McCallum, Chris McKenney, Jeff McMahon, P. Daniel Meerburg, Joel Meyers, Amber Miller, Mark Mirmelstein, Kavilan Moodley, Moritz Munchmeyer, Charles Munson, Sigurd Naess, Federico Nati, Martin Navaroli, Laura Newburgh, Ho Nam Nguyen, Michael Niemack, Haruki Nishino, John Orlowski-Scherer, Lyman Page, Bruce Partridge, Julien Peloton, Francesca Perrotta, Lucio Piccirillo, Giampaolo Pisano, Davide Poletti, Roberto Puddu, Giuseppe Puglisi, Chris Raum, Christian L. Reichardt, Mathieu Remazeilles, Yoel Rephaeli, Dominik Riechers, Felipe Rojas, Anirban Roy, Sharon Sadeh, Yuki Sakurai, Maria Salatino, Mayuri Sathyanarayana Rao, Emmanuel Schaan, Marcel Schmittfull, Neelima Sehgal, Joseph Seibert, Uros Seljak, Blake Sherwin, Meir Shimon, Carlos Sierra, Jonathan Sievers, Precious Sikhosana, Maximiliano Silva-Feaver, Sara M. Simon, Adrian Sinclair, Praween Siritanasak, Kendrick Smith, Stephen R. Smith, David Spergel, Suzanne T. Staggs, George Stein, Jason R. Stevens, Radek Stompor, Aritoki Suzuki, Osamu Tajima, Satoru Takakura, Grant Teply, Daniel B. Thomas, Ben Thorne, Robert Thornton, Hy Trac, Calvin Tsai, Carole Tucker, Joel Ullom, Sunny Vagnozzi, Alexander van Engelen, Jeff Van Lanen, Daniel D. Van Winkle, Eve M. Vavagiakis, Clara Vergès, Michael Vissers, Kasey Wagoner, Samantha Walker, Jon Ward, Ben Westbrook, Nathan Whitehorn, Jason Williams, Joel Williams, Edward J. Wollack, Zhilei Xu, Byeonghee Yu, Cyndia Yu, Fernando Zago, Hezi Zhang, Ningfeng Zhu
With up to an order of magnitude lower polarization noise than maps from the Planck satellite, the high-resolution sky maps will constrain cosmological parameters derived from the damping tail, gravitational lensing of the microwave background, the primordial bispectrum, and the thermal and kinematic Sunyaev-Zel'dovich effects, and will aid in delensing the large-angle polarization signal to measure the tensor-to-scalar ratio.
Cosmology and Nongalactic Astrophysics
no code implementations • 11 May 2018 • Philippe Berger, George Stein
For modern large-scale structure survey techniques it has become standard practice to test data analysis pipelines on large suites of mock simulations, a task which is currently prohibitively expensive for full N-body simulations.