Zobrazeno 1 - 10
of 531
pro vyhledávání: '"Vogelstein, Joshua"'
Autor:
De Silva, Ashwin, Ramesh, Rahul, Yang, Rubing, Yu, Siyu, Vogelstein, Joshua T, Chaudhari, Pratik
In real-world applications, the distribution of the data, and our goals, evolve over time. The prevailing theoretical framework for studying machine learning, namely probably approximately correct (PAC) learning, largely ignores time. As a consequenc
Externí odkaz:
http://arxiv.org/abs/2411.00109
Autor:
Bridgeford, Eric W., Chung, Jaewon, Gilbert, Brian, Panda, Sambit, Li, Adam, Shen, Cencheng, Badea, Alexandra, Caffo, Brian, Vogelstein, Joshua T.
Causal inference studies whether the presence of a variable influences an observed outcome. As measured by quantities such as the "average treatment effect," this paradigm is employed across numerous biological fields, from vaccine and drug developme
Externí odkaz:
http://arxiv.org/abs/2307.13868
Natural intelligences (NIs) thrive in a dynamic world - they learn quickly, sometimes with only a few samples. In contrast, artificial intelligences (AIs) typically learn with a prohibitive number of training samples and computational power. What des
Externí odkaz:
http://arxiv.org/abs/2303.17589
Autor:
Athey, Thomas L., Tward, Daniel J., Mueller, Ulrich, Younes, Laurent, Vogelstein, Joshua T., Miller, Michael I.
The international neuroscience community is building the first comprehensive atlases of brain cell types to understand how the brain functions from a higher resolution, and more integrated perspective than ever before. In order to build these atlases
Externí odkaz:
http://arxiv.org/abs/2303.09649
Autor:
Chen, Tianyi, Park, Youngser, Saad-Eldin, Ali, Lubberts, Zachary, Athreya, Avanti, Pedigo, Benjamin D., Vogelstein, Joshua T., Puppo, Francesca, Silva, Gabriel A., Muotri, Alysson R., Yang, Weiwei, White, Christopher M., Priebe, Carey E.
Recent advancements have been made in the development of cell-based in-vitro neuronal networks, or organoids. In order to better understand the network structure of these organoids, a super-selective algorithm has been proposed for inferring the effe
Externí odkaz:
http://arxiv.org/abs/2303.04871
We propose a class of models based on Fisher's Linear Discriminant (FLD) in the context of domain adaptation. The class is the convex combination of two hypotheses: i) an average hypothesis representing previously seen source tasks and ii) a hypothes
Externí odkaz:
http://arxiv.org/abs/2302.14186
Publikováno v:
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:7366-7389, 2023
We expect the generalization error to improve with more samples from a similar task, and to deteriorate with more samples from an out-of-distribution (OOD) task. In this work, we show a counter-intuitive phenomenon: the generalization error of a task
Externí odkaz:
http://arxiv.org/abs/2208.10967
Autor:
Wang, Qingyang, Powell, Michael A., Geisa, Ali, Bridgeford, Eric, Priebe, Carey E., Vogelstein, Joshua T.
Why do brains have inhibitory connections? Why do deep networks have negative weights? We propose an answer from the perspective of representation capacity. We believe representing functions is the primary role of both (i) the brain in natural intell
Externí odkaz:
http://arxiv.org/abs/2208.03211
Autor:
Dey, Jayanta, Xu, Haoyin, LeVine, Will, De Silva, Ashwin, Tomita, Tyler M., Geisa, Ali, Chu, Tiffany, Desman, Jacob, Vogelstein, Joshua T.
Deep discriminative approaches like random forests and deep neural networks have recently found applications in many important real-world scenarios. However, deploying these learning algorithms in safety-critical applications raises concerns, particu
Externí odkaz:
http://arxiv.org/abs/2201.13001
Autor:
De Silva, Ashwin, Ramesh, Rahul, Ungar, Lyle, Shuler, Marshall Hussain, Cowan, Noah J., Platt, Michael, Li, Chen, Isik, Leyla, Roh, Seung-Eon, Charles, Adam, Venkataraman, Archana, Caffo, Brian, How, Javier J., Kebschull, Justus M, Krakauer, John W., Bichuch, Maxim, Kinfu, Kaleab Alemayehu, Yezerets, Eva, Jayaraman, Dinesh, Shin, Jong M., Villar, Soledad, Phillips, Ian, Priebe, Carey E., Hartung, Thomas, Miller, Michael I., Dey, Jayanta, Ningyuan, Huang, Eaton, Eric, Etienne-Cummings, Ralph, Ogburn, Elizabeth L., Burns, Randal, Osuagwu, Onyema, Mensh, Brett, Muotri, Alysson R., Brown, Julia, White, Chris, Yang, Weiwei, Rusu, Andrei A., Verstynen, Timothy, Kording, Konrad P., Chaudhari, Pratik, Vogelstein, Joshua T.
Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the assumption that the future will be identical to the past in distribut
Externí odkaz:
http://arxiv.org/abs/2201.07372