Scalable bayesian optimization using deep neural networks
…, N Sundaram, M Patwary, M Prabhat… - International …, 2015 - proceedings.mlr.press
Bayesian optimization is an effective methodology for the global optimization of functions
with expensive evaluations. It relies on querying a distribution over functions defined by a …
with expensive evaluations. It relies on querying a distribution over functions defined by a …
Extremeweather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
…, S Ebrahimi Kahou, M Prabhat… - Advances in neural …, 2017 - proceedings.neurips.cc
… the "same" storms across time (Prabhat et al., 2012). These storm … where M is the total
number of pixels in an image. … M is model; P is millions of parameters; and λ weights the amount …
number of pixels in an image. … M is model; P is millions of parameters; and λ weights the amount …
The atmospheric river tracking method intercomparison project (ARTMIP): Quantifying uncertainties in atmospheric river climatology
…, G Muszynski, PD Nguyen, M Prabhat… - Journal of …, 2019 - Wiley Online Library
Atmospheric rivers (ARs) are now widely known for their association with high‐impact
weather events and long‐term water supply in many regions. Researchers within the scientific …
weather events and long‐term water supply in many regions. Researchers within the scientific …
A multiplatform study of I/O behavior on petascale supercomputers
We examine the I/O behavior of thousands of supercomputing applications "in the wild," by
analyzing the Darshan logs of over a million jobs representing a combined total of six years of …
analyzing the Darshan logs of over a million jobs representing a combined total of six years of …
Meshfreeflownet: A physics-constrained deep continuous space-time super-resolution framework
…, HA Tchelepi, P Marcus, M Prabhat… - … Conference for High …, 2020 - ieeexplore.ieee.org
We propose MESHFREEFLOWNET, a novel deep learning-based super-resolution framework
to generate continuous (grid-free) spatio-temporal solutions from the low-resolution inputs…
to generate continuous (grid-free) spatio-temporal solutions from the low-resolution inputs…
[HTML][HTML] Prevalence study of oral mucosal lesions, mucosal variants, and treatment required for patients reporting to a dental school in North India: In accordance with …
The aim of the study was to evaluate the prevalence of oral mucosal lesions (OML) in adult
patients reporting to the dental outpatient department at the Institute of Dental Studies and …
patients reporting to the dental outpatient department at the Institute of Dental Studies and …
Deep-hurricane-tracker: Tracking and forecasting extreme climate events
…, SE Kahou, K Kashinath, M Prabhat - 2019 IEEE winter …, 2019 - ieeexplore.ieee.org
Tracking and predicting extreme events in large-scale spatio-temporal climate data are long
standing challenges in climate science. In this paper, we propose Convolutional LSTM (…
standing challenges in climate science. In this paper, we propose Convolutional LSTM (…
[HTML][HTML] Computed tomography based forensic gender determination by measuring the size and volume of the maxillary sinuses
M Prabhat, S Rai, M Kaur, K Prabhat… - Journal of forensic …, 2016 - ncbi.nlm.nih.gov
Purpose: Identification of human body or remains after death is a forensic procedure, which
is difficult to perform and is mandatory by law and in compliance with social norms. Sexing …
is difficult to perform and is mandatory by law and in compliance with social norms. Sexing …
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
…, A Tartakovsky, M Houston, M Prabhat… - 2019 IEEE/ACM …, 2019 - ieeexplore.ieee.org
Uncertainty quantification for forward and inverse problems is a central challenge across
physical and biomedical disciplines. We address this challenge for the problem of modeling …
physical and biomedical disciplines. We address this challenge for the problem of modeling …
Efficient probabilistic inference in the quest for physics beyond the standard model
… Ng, and Prabhat. Improvements to inference compilation for probabilistic programming in
large-scale scientific simulators. In Neural Information Processing Systems (NIPS) 2017 …
large-scale scientific simulators. In Neural Information Processing Systems (NIPS) 2017 …