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pro vyhledávání: '"Domke, A"'
MPI_Alltoallv generalizes the uniform all-to-all communication (MPI_Alltoall) by enabling the exchange of data blocks of varied sizes among processes. This function plays a crucial role in many applications, such as FFT computation and relational alg
Externí odkaz:
http://arxiv.org/abs/2411.02581
Bayesian reasoning in linear mixed-effects models (LMMs) is challenging and often requires advanced sampling techniques like Markov chain Monte Carlo (MCMC). A common approach is to write the model in a probabilistic programming language and then sam
Externí odkaz:
http://arxiv.org/abs/2410.24079
The abundant demand for deep learning compute resources has created a renaissance in low precision hardware. Going forward, it will be essential for simulation software to run on this new generation of machines without sacrificing scientific fidelity
Externí odkaz:
http://arxiv.org/abs/2407.13299
Autor:
Shannon, Elliot S., Finley, Andrew O., May, Paul B., Domke, Grant M., Andersen, Hans-Erik, Gaines III, George C., Banerjee, Sudipto
National Forest Inventory (NFI) programs can provide vital information on the status, trend, and change in forest parameters. These programs are being increasingly asked to provide forest parameter estimates for spatial and temporal extents smaller t
Externí odkaz:
http://arxiv.org/abs/2407.09909
Autor:
Agrawal, Abhinav, Domke, Justin
Predictive posterior densities (PPDs) are of interest in approximate Bayesian inference. Typically, these are estimated by simple Monte Carlo (MC) averages using samples from the approximate posterior. We observe that the signal-to-noise ratio (SNR)
Externí odkaz:
http://arxiv.org/abs/2405.19747
The United States national forest inventory (NFI) serves as the foundation for forest aboveground biomass (AGB) and carbon accounting across the nation. These data enable design-based estimates of forest carbon stocks and stock-changes at state and r
Externí odkaz:
http://arxiv.org/abs/2405.04507
Simulation-based inference has been popular for amortized Bayesian computation. It is typical to have more than one posterior approximation, from different inference algorithms, different architectures, or simply the randomness of initialization and
Externí odkaz:
http://arxiv.org/abs/2310.17009
Autor:
Blach, Nils, Besta, Maciej, De Sensi, Daniele, Domke, Jens, Harake, Hussein, Li, Shigang, Iff, Patrick, Konieczny, Marek, Lakhotia, Kartik, Kubicek, Ales, Ferrari, Marcel, Petrini, Fabrizio, Hoefler, Torsten
Publikováno v:
Proceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI '24) Santa Clara, CA, USA April 16-18, 2024
Novel low-diameter network topologies such as Slim Fly (SF) offer significant cost and power advantages over the established Fat Tree, Clos, or Dragonfly. To spearhead the adoption of low-diameter networks, we design, implement, deploy, and evaluate
Externí odkaz:
http://arxiv.org/abs/2310.03742
Black-box variational inference is widely used in situations where there is no proof that its stochastic optimization succeeds. We suggest this is due to a theoretical gap in existing stochastic optimization proofs: namely the challenge of gradient e
Externí odkaz:
http://arxiv.org/abs/2306.03638
Autor:
Yao, Yuling, Domke, Justin
To check the accuracy of Bayesian computations, it is common to use rank-based simulation-based calibration (SBC). However, SBC has drawbacks: The test statistic is somewhat ad-hoc, interactions are difficult to examine, multiple testing is a challen
Externí odkaz:
http://arxiv.org/abs/2305.14593