1 citations · 2 across the 6 of their papers we have counts for
6 papers
DEF: Diffusion-augmented Ensemble Forecasting
David Millard, Arielle Carr, Stéphane Gaudreault +1
We present DEF (\textbf{\ul{D}}iffusion-augmented \textbf{\ul{E}}nsemble \textbf{\ul{F}}orecasting), a novel approach for generating initial condition perturbations. Modern approac…
PEARL: Preconditioner Enhancement through Actor-critic Reinforcement Learning
David Millard, Arielle Carr, Stéphane Gaudreault +1
We present PEARL (Preconditioner Enhancement through Actor-critic Reinforcement Learning), a novel approach to learning matrix preconditioners. Existing preconditioners such as Jac…
Empirical Perturbation Analysis of Linear System Solvers from a Data Poisoning Perspective
Yixin Liu, Arielle Carr, Lichao Sun
The perturbation analysis of linear solvers applied to systems arising broadly in machine learning settings -- for instance, when using linear regression models -- establishes an i…
A Dynamic Weighting Strategy to Mitigate Worker Node Failure in Distributed Deep Learning
Yuesheng Xu, Arielle Carr
The increasing complexity of deep learning models and the demand for processing vast amounts of data make the utilization of large-scale distributed systems for efficient training…
Deep Learning for Koopman Operator Estimation in Idealized Atmospheric Dynamics
David Millard, Arielle Carr, Stéphane Gaudreault
Deep learning is revolutionizing weather forecasting, with new data-driven models achieving accuracy on par with operational physical models for medium-term predictions. However, t…
Optimization of Approximate Maps for Linear Systems Arising in Discretized PDEs
Rishad Islam, Arielle Carr, Colin Jacobs
Generally, discretization of partial differential equations (PDEs) creates a sequence of linear systems with well-known and structured sparsity…