Zobrazeno 1 - 10
of 36 673
pro vyhledávání: '"A., Leroux"'
Autor:
Du, Hongyu, Leroux, Andrew
We propose two novel approaches for estimating time-varying effects of functional predictors within a linear functional Cox model framework. This model allows for time-varying associations of a functional predictor observed at baseline, estimated usi
Externí odkaz:
http://arxiv.org/abs/2412.14478
Autor:
Leroux, Catherine, Lin, Sophia F., Bienias, Przemyslaw, Sankar, Krishanu R., Benhemou, Asmae, Kubica, Aleksander, Iverson, Joseph K.
One of the critical challenges solid-state quantum processors face is the presence of fabrication imperfections and two-level systems, which render certain qubits and gates either inoperable or much noisier than tolerable by quantum error correction
Externí odkaz:
http://arxiv.org/abs/2412.11504
Learning from Demonstrations (LfD) and Reinforcement Learning (RL) have enabled robot agents to accomplish complex tasks. Reward Machines (RMs) enhance RL's capability to train policies over extended time horizons by structuring high-level task infor
Externí odkaz:
http://arxiv.org/abs/2412.10096
Autor:
Jin, Ying, Leroux, Andrew
Dynamic prediction, which typically refers to the prediction of future outcomes using historical records, is often of interest in biomedical research. For datasets with large sample sizes, high measurement density, and complex correlation structures,
Externí odkaz:
http://arxiv.org/abs/2412.02014
Autor:
Lu, Yu, Zhou, Xinkai, Cui, Erjia, Rogers, Dustin, Crainiceanu, Ciprian M., Wrobel, Julia, Leroux, Andrew
We propose generalized conditional functional principal components analysis (GC-FPCA) for the joint modeling of the fixed and random effects of non-Gaussian functional outcomes. The method scales up to very large functional data sets by estimating th
Externí odkaz:
http://arxiv.org/abs/2411.10312
In this paper, we present a deterministic algorithm to count the low-weight codewords of punctured and shortened pure and pre-transformed polar codes. The method first evaluates the weight properties of punctured/shortened polar cosets. Then, a metho
Externí odkaz:
http://arxiv.org/abs/2411.05433
Analog Content Addressable Memories (aCAMs) have proven useful for associative in-memory computing applications like Decision Trees, Finite State Machines, and Hyper-dimensional Computing. While non-volatile implementations using FeFETs and ReRAM dev
Externí odkaz:
http://arxiv.org/abs/2410.09755
Autor:
Leroux, Nathan, Manea, Paul-Philipp, Sudarshan, Chirag, Finkbeiner, Jan, Siegel, Sebastian, Strachan, John Paul, Neftci, Emre
Transformer networks, driven by self-attention, are central to Large Language Models. In generative Transformers, self-attention uses cache memory to store token projections, avoiding recomputation at each time step. However, GPU-stored projections m
Externí odkaz:
http://arxiv.org/abs/2409.19315
Autor:
Putterman, Harald, Noh, Kyungjoo, Hann, Connor T., MacCabe, Gregory S., Aghaeimeibodi, Shahriar, Patel, Rishi N., Lee, Menyoung, Jones, William M., Moradinejad, Hesam, Rodriguez, Roberto, Mahuli, Neha, Rose, Jefferson, Owens, John Clai, Levine, Harry, Rosenfeld, Emma, Reinhold, Philip, Moncelsi, Lorenzo, Alcid, Joshua Ari, Alidoust, Nasser, Arrangoiz-Arriola, Patricio, Barnett, James, Bienias, Przemyslaw, Carson, Hugh A., Chen, Cliff, Chen, Li, Chinkezian, Harutiun, Chisholm, Eric M., Chou, Ming-Han, Clerk, Aashish, Clifford, Andrew, Cosmic, R., Curiel, Ana Valdes, Davis, Erik, DeLorenzo, Laura, D'Ewart, J. Mitchell, Diky, Art, D'Souza, Nathan, Dumitrescu, Philipp T., Eisenmann, Shmuel, Elkhouly, Essam, Evenbly, Glen, Fang, Michael T., Fang, Yawen, Fling, Matthew J., Fon, Warren, Garcia, Gabriel, Gorshkov, Alexey V., Grant, Julia A., Gray, Mason J., Grimberg, Sebastian, Grimsmo, Arne L., Haim, Arbel, Hand, Justin, He, Yuan, Hernandez, Mike, Hover, David, Hung, Jimmy S. C., Hunt, Matthew, Iverson, Joe, Jarrige, Ignace, Jaskula, Jean-Christophe, Jiang, Liang, Kalaee, Mahmoud, Karabalin, Rassul, Karalekas, Peter J., Keller, Andrew J., Khalajhedayati, Amirhossein, Kubica, Aleksander, Lee, Hanho, Leroux, Catherine, Lieu, Simon, Ly, Victor, Madrigal, Keven Villegas, Marcaud, Guillaume, McCabe, Gavin, Miles, Cody, Milsted, Ashley, Minguzzi, Joaquin, Mishra, Anurag, Mukherjee, Biswaroop, Naghiloo, Mahdi, Oblepias, Eric, Ortuno, Gerson, Pagdilao, Jason, Pancotti, Nicola, Panduro, Ashley, Paquette, JP, Park, Minje, Peairs, Gregory A., Perello, David, Peterson, Eric C., Ponte, Sophia, Preskill, John, Qiao, Johnson, Refael, Gil, Resnick, Rachel, Retzker, Alex, Reyna, Omar A., Runyan, Marc, Ryan, Colm A., Sahmoud, Abdulrahman, Sanchez, Ernesto, Sanil, Rohan, Sankar, Krishanu, Sato, Yuki, Scaffidi, Thomas, Siavoshi, Salome, Sivarajah, Prasahnt, Skogland, Trenton, Su, Chun-Ju, Swenson, Loren J., Teo, Stephanie M., Tomada, Astrid, Torlai, Giacomo, Wollack, E. Alex, Ye, Yufeng, Zerrudo, Jessica A., Zhang, Kailing, Brandão, Fernando G. S. L., Matheny, Matthew H., Painter, Oskar
In order to solve problems of practical importance, quantum computers will likely need to incorporate quantum error correction, where a logical qubit is redundantly encoded in many noisy physical qubits. The large physical-qubit overhead typically as
Externí odkaz:
http://arxiv.org/abs/2409.13025
Specifying tasks for robotic systems traditionally requires coding expertise, deep domain knowledge, and significant time investment. While learning from demonstration offers a promising alternative, existing methods often struggle with tasks of long
Externí odkaz:
http://arxiv.org/abs/2409.07091