Zobrazeno 1 - 10
of 253
pro vyhledávání: '"Dowling, Matthew"'
Learning shared structure across environments facilitates rapid learning and adaptive behavior in neural systems. This has been widely demonstrated and applied in machine learning to train models that are capable of generalizing to novel settings. Ho
Externí odkaz:
http://arxiv.org/abs/2410.05454
Autor:
Vermani, Ayesha, Dowling, Matthew, Jeon, Hyungju, Jordan, Ian, Nassar, Josue, Bernaerts, Yves, Zhao, Yuan, Van Vaerenbergh, Steven, Park, Il Memming
Function and dysfunctions of neural systems are tied to the temporal evolution of neural states. The current limitations in showing their causal role stem largely from the absence of tools capable of probing the brain's internal state in real-time. T
Externí odkaz:
http://arxiv.org/abs/2409.01280
State-space graphical models and the variational autoencoder framework provide a principled apparatus for learning dynamical systems from data. State-of-the-art probabilistic approaches are often able to scale to large problems at the cost of flexibi
Externí odkaz:
http://arxiv.org/abs/2403.01371
Latent Gaussian process (GP) models are widely used in neuroscience to uncover hidden state evolutions from sequential observations, mainly in neural activity recordings. While latent GP models provide a principled and powerful solution in theory, th
Externí odkaz:
http://arxiv.org/abs/2306.01802
Latent variable models have become instrumental in computational neuroscience for reasoning about neural computation. This has fostered the development of powerful offline algorithms for extracting latent neural trajectories from neural recordings. H
Externí odkaz:
http://arxiv.org/abs/2305.11278
We present the class of Hida-Mat\'ern kernels, which is the canonical family of covariance functions over the entire space of stationary Gauss-Markov Processes. It extends upon Mat\'ern kernels, by allowing for flexible construction of priors over pr
Externí odkaz:
http://arxiv.org/abs/2107.07098
Autor:
Dorken-Gallastegi, Ander, Naar, Leon, Argandykov, Dias, Lagazzi, Emanuele, Dowling, Matthew, Montero, Paula, Wallace, Brandon, Pallotta, Jessica B., Beagle, John, Breen, Kerry, Velmahos, George C., Duggan, Michael J., King, David R.
Publikováno v:
In Surgery April 2024 175(4):1189-1197
Publikováno v:
Phys. Rev. D 103, 111301 (2021)
Recently, $O(\alpha^3)$ corrections to the muon decay rate and $O(\alpha_s^3)$ to heavy quark decays have been determined by Fael, Sch\"onwald, and Steinhauser. This is the first such perturbative improvement of these important quantities in more tha
Externí odkaz:
http://arxiv.org/abs/2104.05804
A fundamental problem in statistical neuroscience is to model how neurons encode information by analyzing electrophysiological recordings. A popular and widely-used approach is to fit the spike trains with an autoregressive point process model. These
Externí odkaz:
http://arxiv.org/abs/2009.01362
Autor:
Naar, Leon, Dorken Gallastegi, Ander, Dowling, Matthew, Mashbari, Hassan Naser A., Wallace, Brandon, Bankhead-Kendall, Brittany, Beagle, John, Pallotta, Jessica B., Breen, Kerry, Velmahos, George C., Duggan, Michael J., King, Col David R.
Publikováno v:
In Surgery July 2022 172(1):421-426