Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Cannella, Chris"'
Transformers have demonstrated remarkable efficacy in forecasting time series data. However, their extensive dependence on self-attention mechanisms demands significant computational resources, thereby limiting their practical applicability across di
Externí odkaz:
http://arxiv.org/abs/2310.01720
Autor:
Cannella, Chris, Tarokh, Vahid
Current objective functions used for training neural MCMC proposal distributions implicitly rely on architectural restrictions to yield sensible optimization results, which hampers the development of highly expressive neural MCMC proposal architectur
Externí odkaz:
http://arxiv.org/abs/2106.02104
We introduce Projected Latent Markov Chain Monte Carlo (PL-MCMC), a technique for sampling from the high-dimensional conditional distributions learned by a normalizing flow. We prove that a Metropolis-Hastings implementation of PL-MCMC asymptotically
Externí odkaz:
http://arxiv.org/abs/2007.06140
Autor:
Kasliwal, Mansi M., Anand, Shreya, Ahumada, Tomas, Stein, Robert, Carracedo, Ana Sagues, Andreoni, Igor, Coughlin, Michael W., Singer, Leo P., Kool, Erik C., De, Kishalay, Kumar, Harsh, AlMualla, Mouza, Yao, Yuhan, Bulla, Mattia, Dobie, Dougal, Reusch, Simeon, Perley, Daniel A., Cenko, S. Bradley, Bhalerao, Varun, Kaplan, David L., Sollerman, Jesper, Goobar, Ariel, Copperwheat, Christopher M., Bellm, Eric C., Anupama, G. C., Corsi, Alessandra, Nissanke, Samaya, Agudo, Ivan, Bagdasaryan, Ashot, Barway, Sudhanshu, Belicki, Justin, Bloom, Joshua S., Bolin, Bryce, Buckley, David A. H., Burdge, Kevin B., Burruss, Rick, Caballero-Garcıa, Maria D., Cannella, Chris, Castro-Tirado, Alberto J., Cook, David O., Cooke, Jeff, Cunningham, Virginia, Dahiwale, Aishwarya, Deshmukh, Kunal, Dichiara, Simone, Duev, Dmitry A., Dutta, Anirban, Feeney, Michael, Franckowiak, Anna, Frederick, Sara, Fremling, Christoffer, Gal-Yam, Avishay, Gatkine, Pradip, Ghosh, Shaon, Goldstein, Daniel A., Golkhou, V. Zach, Graham, Matthew J., Graham, Melissa L., Hankins, Matthew J., Helou, George, Hu, Youdong, Ip, Wing-Huen, Jaodand, Amruta, Karambelkar, Viraj, Kong, Albert K. H., Kowalski, Marek, Khandagale, Maitreya, Kulkarni, S. R., Kumar, Brajesh, Laher, Russ R., Li, K. L., Mahabal, Ashish, Masci, Frank J., Miller, Adam A., Mogotsi, Moses, Mohite, Siddharth, Mooley, Kunal, Mroz, Przemek, Newman, Jeffrey A., Ngeow, Chow-Choong, Oates, Samantha R., Patil, Atharva Sunil, Pandey, Shashi B., Pavana, M., Pian, Elena, Riddle, Reed, Sanchez-Ramırez, Ruben, Sharma, Yashvi, Singh, Avinash, Smith, Roger, Soumagnac, Maayane T., Taggart, Kirsty, Tan, Hanjie, Tzanidakis, Anastasios, Troja, Eleonora, Valeev, Azamat F., Walters, Richard, Waratkar, Gaurav, Webb, Sara, Yu, Po-Chieh, Zhang, Bin-Bin, Zhou, Rongpu, Zolkower, Jeffry
We present a systematic search for optical counterparts to 13 gravitational wave (GW) triggers involving at least one neutron star during LIGO/Virgo's third observing run. We searched binary neutron star (BNS) and neutron star black hole (NSBH) merge
Externí odkaz:
http://arxiv.org/abs/2006.11306
Autor:
Soumagnac, Maayane T., Ofek, Eran O., Liang, Jingyi, Gal-yam, Avishay, Nugent, Peter, Yang, Yi, Cenko, S. Bradley, Sollerman, Jesper, Perley, Daniel A., Andreoni, Igor, Barbarino, Cristina, Burdge, Kevin B., Bruch, Rachel J., De, Kishalay, Dugas, Alison, Fremling, Christoffer, Graham, Melissa L., Hankins, Matthew J., Strotjohann, Nora Linn, Moran, Shane, Neill, James D., Schulze, Steve, Shupe, David L., Sipocz, Brigitta M., Taggart, Kirsty, Tartaglia, Leonardo, Walters, Richard, Yan, Lin, Yao, Yuhan, Yaron, Ofer, Bellm, Eric C., Cannella, Chris, Dekany, Richard, Duev, Dmitry A., Feeney, Michael, Frederick, Sara, Graham, Matthew J., Laher, Russ R., Masci, Frank J., Kasliwal, Mansi M., Kowalski, Marek, Miller, Adam A., Rigault, Mickael, Rusholme, Ben
We present a survey of the early evolution of 12 Type IIn supernovae (SNe IIn) in the Ultra-Violet (UV) and visible light. We use this survey to constrain the geometry of the circumstellar material (CSM) surrounding SN IIn explosions, which may shed
Externí odkaz:
http://arxiv.org/abs/2001.05518
In this work, we introduce a new procedure for applying Restricted Boltzmann Machines (RBMs) to missing data inference tasks, based on linearization of the effective energy function governing the distribution of observations. We compare the performan
Externí odkaz:
http://arxiv.org/abs/1910.09122
Autor:
Chatterjee, Deep, Nugent, Peter E., Brady, Patrick R., Cannella, Chris, Kaplan, David L., Kasliwal, Mansi M.
The last couple of decades have seen an emergence of transient detection facilities in various avenues of time domain astronomy which has provided us with a rich dataset of transients. The rates of these transients have implications in star formation
Externí odkaz:
http://arxiv.org/abs/1906.09309
Autor:
Mahabal, Ashish, Rebbapragada, Umaa, Walters, Richard, Masci, Frank J., Blagorodnova, Nadejda, van Roestel, Jan, Ye, Quan-Zhi, Biswas, Rahul, Burdge, Kevin, Chang, Chan-Kao, Duev, Dmitry A., Golkhou, V. Zach, Miller, Adam A., Nordin, Jakob, Ward, Charlotte, Adams, Scott, Bellm, Eric C., Branton, Doug, Bue, Brian, Cannella, Chris, Connolly, Andrew, Dekany, Richard, Feindt, Ulrich, Hung, Tiara, Fortson, Lucy, Frederick, Sara, Fremling, C., Gezari, Suvi, Graham, Matthew, Groom, Steven, Kasliwal, Mansi M., Kulkarni, Shrinivas, Kupfer, Thomas, Lin, Hsing Wen, Lintott, Chris, Lunnan, Ragnhild, Parejko, John, Prince, Thomas A., Riddle, Reed, Rusholme, Ben, Saunders, Nicholas, Sedaghat, Nima, Shupe, David L., Singer, Leo P., Soumagnac, Maayane T., Szkody, Paula, Tachibana, Yutaro, Tirumala, Kushal, van Velzen, Sjoert, Wright, Darryl
The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of the large d
Externí odkaz:
http://arxiv.org/abs/1902.01936
Accurate modeling of galaxy formation in a hierarchical, cold dark matter universe requires the use of sufficiently high-resolution merger trees to obtain convergence in the predicted properties of galaxies. When semi-analytic galaxy formation models
Externí odkaz:
http://arxiv.org/abs/1604.02147
Autor:
Cannella, Chris, Tarokh, Vahid
Publikováno v:
2022 26th International Conference on Pattern Recognition (ICPR).
Current objective functions used for training neural MCMC proposal distributions implicitly rely on architectural restrictions to yield sensible optimization results, which hampers the development of highly expressive neural MCMC proposal architectur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0f72d3cc93e6fb697744d696bfee92b4
https://doi.org/10.1109/icpr56361.2022.9956603
https://doi.org/10.1109/icpr56361.2022.9956603
