Zobrazeno 1 - 10
of 217
pro vyhledávání: '"Bouchard, Kristofer"'
There is overwhelming evidence that cognition, perception, and action rely on feedback control. However, if and how neural population dynamics are amenable to different control strategies is poorly understood, in large part because machine learning m
Externí odkaz:
http://arxiv.org/abs/2408.05875
Autor:
Avaylon, Matthew, Ly, Ryan, Tritt, Andrew, Dichter, Benjamin, Bouchard, Kristofer E., Mungall, Christopher J., Ruebel, Oliver
Across many domains, large swaths of digital assets are being stored across distributed data repositories, e.g., the DANDI Archive [8]. The distribution and diversity of these repositories impede researchers from formally defining terminology within
Externí odkaz:
http://arxiv.org/abs/2406.00063
Autor:
Joachimiak, Marcin P., Miller, Mark A., Caufield, J. Harry, Ly, Ryan, Harris, Nomi L., Tritt, Andrew, Mungall, Christopher J., Bouchard, Kristofer E.
The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims
Externí odkaz:
http://arxiv.org/abs/2404.03044
Autor:
Yoo, S. J. Ben, El-Srouji, Luis, Datta, Suman, Yu, Shimeng, Incorvia, Jean Anne, Salleo, Alberto, Sorger, Volker, Hu, Juejun, Kimerling, Lionel C, Bouchard, Kristofer, Geng, Joy, Chaudhuri, Rishidev, Ranganath, Charan, O'Reilly, Randall
The human brain has immense learning capabilities at extreme energy efficiencies and scale that no artificial system has been able to match. For decades, reverse engineering the brain has been one of the top priorities of science and technology resea
Externí odkaz:
http://arxiv.org/abs/2403.19724
The processing and analysis of computed tomography (CT) imaging is important for both basic scientific development and clinical applications. In AutoCT, we provide a comprehensive pipeline that integrates an end-to-end automatic preprocessing, regist
Externí odkaz:
http://arxiv.org/abs/2310.17780
Autor:
Martin, Hector Garcia, Radivojevic, Tijana, Zucker, Jeremy, Bouchard, Kristofer, Sustarich, Jess, Peisert, Sean, Arnold, Dan, Hillson, Nathan, Babnigg, Gyorgy, Marti, Jose Manuel, Mungall, Christopher J., Beckham, Gregg T., Waldburger, Lucas, Carothers, James, Sundaram, ShivShankar, Agarwal, Deb, Simmons, Blake A., Backman, Tyler, Banerjee, Deepanwita, Tanjore, Deepti, Ramakrishnan, Lavanya, Singh, Anup
Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is pr
Externí odkaz:
http://arxiv.org/abs/2210.09085
Autor:
Huerta, E. A., Blaiszik, Ben, Brinson, L. Catherine, Bouchard, Kristofer E., Diaz, Daniel, Doglioni, Caterina, Duarte, Javier M., Emani, Murali, Foster, Ian, Fox, Geoffrey, Harris, Philip, Heinrich, Lukas, Jha, Shantenu, Katz, Daniel S., Kindratenko, Volodymyr, Kirkpatrick, Christine R., Lassila-Perini, Kati, Madduri, Ravi K., Neubauer, Mark S., Psomopoulos, Fotis E., Roy, Avik, Rübel, Oliver, Zhao, Zhizhen, Zhu, Ruike
Publikováno v:
Scientific Data 10, 487 (2023)
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles w
Externí odkaz:
http://arxiv.org/abs/2210.08973
Unsupervised learning plays an important role in many fields, such as artificial intelligence, machine learning, and neuroscience. Compared to static data, methods for extracting low-dimensional structure for dynamic data are lagging. We developed a
Externí odkaz:
http://arxiv.org/abs/2203.02051
Autor:
Pion-Tonachini, Luca, Bouchard, Kristofer, Martin, Hector Garcia, Peisert, Sean, Holtz, W. Bradley, Aswani, Anil, Dwivedi, Dipankar, Wainwright, Haruko, Pilania, Ghanshyam, Nachman, Benjamin, Marrone, Babetta L., Falco, Nicola, Prabhat, Arnold, Daniel, Wolf-Yadlin, Alejandro, Powers, Sarah, Climer, Sharlee, Jackson, Quinn, Carlson, Ty, Sohn, Michael, Zwart, Petrus, Kumar, Neeraj, Justice, Amy, Tomlin, Claire, Jacobson, Daniel, Micklem, Gos, Gkoutos, Georgios V., Bickel, Peter J., Cazier, Jean-Baptiste, Müller, Juliane, Webb-Robertson, Bobbie-Jo, Stevens, Rick, Anderson, Mark, Kreutz-Delgado, Ken, Mahoney, Michael W., Brown, James B.
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering pa
Externí odkaz:
http://arxiv.org/abs/2111.13786
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model
Autor:
Meng, Rui, Bouchard, Kristofer
Many modern time-series datasets contain large numbers of output response variables sampled for prolonged periods of time. For example, in neuroscience, the activities of 100s-1000's of neurons are recorded during behaviors and in response to sensory
Externí odkaz:
http://arxiv.org/abs/2106.13379