Zobrazeno 1 - 10
of 561
pro vyhledávání: '"Brooks, Dana"'
It is challenging for generative models to learn a distribution over graphs because of the lack of permutation invariance: nodes may be ordered arbitrarily across graphs, and standard graph alignment is combinatorial and notoriously expensive. We pro
Externí odkaz:
http://arxiv.org/abs/2301.11273
Autor:
Torop, Max, Ghimire, Sandesh, Liu, Wenqian, Brooks, Dana H., Camps, Octavia, Rajadhyaksha, Milind, Dy, Jennifer, Kose, Kivanc
There are limited works showing the efficacy of unsupervised Out-of-Distribution (OOD) methods on complex medical data. Here, we present preliminary findings of our unsupervised OOD detection algorithm, SimCLR-LOF, as well as a recent state of the ar
Externí odkaz:
http://arxiv.org/abs/2111.04807
Autor:
Singh, Ashutosh, Westlin, Christiana, Eisenbarth, Hedwig, Losin, Elizabeth A. Reynolds, Andrews-Hanna, Jessica R., Wager, Tor D., Satpute, Ajay B., Barrett, Lisa Feldman, Brooks, Dana H., Erdogmus, Deniz
For the last several decades, emotion research has attempted to identify a "biomarker" or consistent pattern of brain activity to characterize a single category of emotion (e.g., fear) that will remain consistent across all instances of that category
Externí odkaz:
http://arxiv.org/abs/2110.12392
Autor:
Schuler, Steffen, Schaufelberger, Matthias, Bear, Laura R., Bergquist, Jake A., Cluitmans, Matthijs J. M., Coll-Font, Jaume, Onak, Önder N., Zenger, Brian, Loewe, Axel, MacLeod, Rob S., Brooks, Dana H., Dössel, Olaf
Publikováno v:
IEEE Transactions on Biomedical Engineering, 2021
Objective: To investigate cardiac activation maps estimated using electrocardiographic imaging and to find methods reducing line-of-block (LoB) artifacts, while preserving real LoBs. Methods: Body surface potentials were computed for 137 simulated ve
Externí odkaz:
http://arxiv.org/abs/2108.06602
Many contemporary machine learning models require extensive tuning of hyperparameters to perform well. A variety of methods, such as Bayesian optimization, have been developed to automate and expedite this process. However, tuning remains extremely c
Externí odkaz:
http://arxiv.org/abs/2002.09927
Autor:
Akbar, Md Navid, Yarossi, Mathew, Martinez-Gost, Marc, Sommer, Marc A., Dannhauer, Moritz, Rampersad, Sumientra, Brooks, Dana, Tunik, Eugene, Erdoğmuş, Deniz
A deep neural network (DNN) that can reliably model muscle responses from corresponding brain stimulation has the potential to increase knowledge of coordinated motor control for numerous basic science and applied use cases. Such cases include the un
Externí odkaz:
http://arxiv.org/abs/2002.06250
Autor:
Kose, Kivanc, Bozkurt, Alican, Alessi-Fox, Christi, Gill, Melissa, Longo, Caterina, Pellacani, Giovanni, Dy, Jennifer, Brooks, Dana H., Rajadhyaksha, Milind
In-vivo optical microscopy is advancing into routine clinical practice for non-invasively guiding diagnosis and treatment of cancer and other diseases, and thus beginning to reduce the need for traditional biopsy. However, reading and analysis of the
Externí odkaz:
http://arxiv.org/abs/2001.01005
Autor:
Bozkurt, Alican, Esmaeili, Babak, Tristan, Jean-Baptiste, Brooks, Dana H., Dy, Jennifer G., van de Meent, Jan-Willem
Variational autoencoders optimize an objective that combines a reconstruction loss (the distortion) and a KL term (the rate). The rate is an upper bound on the mutual information, which is often interpreted as a regularizer that controls the degree o
Externí odkaz:
http://arxiv.org/abs/1911.04594
An implicit goal in works on deep generative models is that such models should be able to generate novel examples that were not previously seen in the training data. In this paper, we investigate to what extent this property holds for widely employed
Externí odkaz:
http://arxiv.org/abs/1812.09624
Autor:
Westlin, Christiana, Theriault, Jordan E., Katsumi, Yuta, Nieto-Castanon, Alfonso, Kucyi, Aaron, Ruf, Sebastian F., Brown, Sarah M., Pavel, Misha, Erdogmus, Deniz, Brooks, Dana H., Quigley, Karen S., Whitfield-Gabrieli, Susan, Barrett, Lisa Feldman
Publikováno v:
In Trends in Cognitive Sciences March 2023 27(3):246-257
