Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Veillette, Mark"'
Autor:
Veillette, Mark S., Kurdzo, James M., Stepanian, Phillip M., Cho, John Y. N., Samsi, Siddharth, McDonald, Joseph
Weather radar is the primary tool used by forecasters to detect and warn for tornadoes in near-real time. In order to assist forecasters in warning the public, several algorithms have been developed to automatically detect tornadic signatures in weat
Externí odkaz:
http://arxiv.org/abs/2401.16437
Autor:
Veillette, Mark S., Kurdzo, James M., Stepanian, Phillip M., McDonald, Joseph, Samsi, Siddharth, Cho, John Y. N.
Radial velocity estimates provided by Doppler weather radar are critical measurements used by operational forecasters for the detection and monitoring of life-impacting storms. The sampling methods used to produce these measurements are inherently su
Externí odkaz:
http://arxiv.org/abs/2211.13181
Autor:
Gadepally, Vijay, Angelides, Gregory, Barbu, Andrei, Bowne, Andrew, Brattain, Laura J., Broderick, Tamara, Cabrera, Armando, Carl, Glenn, Carter, Ronisha, Cha, Miriam, Cowen, Emilie, Cummings, Jesse, Freeman, Bill, Glass, James, Goldberg, Sam, Hamilton, Mark, Heldt, Thomas, Huang, Kuan Wei, Isola, Phillip, Katz, Boris, Koerner, Jamie, Lin, Yen-Chen, Mayo, David, McAlpin, Kyle, Perron, Taylor, Piou, Jean, Rao, Hrishikesh M., Reynolds, Hayley, Samuel, Kaira, Samsi, Siddharth, Schmidt, Morgan, Shing, Leslie, Simek, Olga, Swenson, Brandon, Sze, Vivienne, Taylor, Jonathan, Tylkin, Paul, Veillette, Mark, Weiss, Matthew L, Wollaber, Allan, Yuditskaya, Sophia, Kepner, Jeremy
Through a series of federal initiatives and orders, the U.S. Government has been making a concerted effort to ensure American leadership in AI. These broad strategy documents have influenced organizations such as the United States Department of the A
Externí odkaz:
http://arxiv.org/abs/2207.07033
Autor:
Rugina, Ileana, Dangovski, Rumen, Veillette, Mark, Khorrami, Pooya, Cheung, Brian, Simek, Olga, Soljačić, Marin
Recent advances in deep learning, in particular enabled by hardware advances and big data, have provided impressive results across a wide range of computational problems such as computer vision, natural language, or reinforcement learning. Many of th
Externí odkaz:
http://arxiv.org/abs/2112.11929
Climate models project an uncertainty range of possible warming scenarios from 1.5 to 5 degree Celsius global temperature increase until 2100, according to the CMIP6 model ensemble. Climate risk management and infrastructure adaptation requires the a
Externí odkaz:
http://arxiv.org/abs/2105.02939
Deep neural networks have shown great success in many diverse fields. The training of these networks can take significant amounts of time, compute and energy. As datasets get larger and models become more complex, the exploration of model architectur
Externí odkaz:
http://arxiv.org/abs/2008.08062
Effective training of Deep Neural Networks requires massive amounts of data and compute. As a result, longer times are needed to train complex models requiring large datasets, which can severely limit research on model development and the exploitatio
Externí odkaz:
http://arxiv.org/abs/1908.10964
Autor:
Veillette, Mark S., Taqqu, Murad S.
Publikováno v:
Bernoulli 2013, Vol. 19, No. 3, 982-1005
This paper studies various distributional properties of the Rosenblatt distribution. We begin by describing a technique for computing the cumulants. We then study the expansion of the Rosenblatt distribution in terms of shifted chi-squared distributi
Externí odkaz:
http://arxiv.org/abs/1307.5990
Autor:
Veillette, Mark S., Taqqu, Murad S.
Consider the sum $Z = \sum_{n=1}^\infty \lambda_n (\eta_n - \mathbb{E}\eta_n)$, where $\eta_n$ are i.i.d.~gamma random variables with shape parameter $r > 0$, and the $\lambda_n$'s are predetermined weights. We study the asymptotic behavior of the ta
Externí odkaz:
http://arxiv.org/abs/1010.3948
Autor:
Veillette, Mark S., Taqqu, Murad S.
We present a method for computing the PDF and CDF of a non-negative infinitely divisible random variable $X$. Our method uses the L\'{e}vy-Khintchine representation of the Laplace transform $\mathbb{E} e^{-\lambda X} = e^{-\phi(\lambda)}$, where $\ph
Externí odkaz:
http://arxiv.org/abs/1005.2614