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Showing 1 to 9 of 9 for “"Differentiable Programming"”.
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The Taichi High-Performance and Differentiable Programming Language for Sparse and Quantized Visual Computing
Using traditional programming languages such as C++ and CUDA, writing high-performance visual computing code is often laborious and requires deep expertise in performance engineering. This implies an undesirable trade-off between performance and productivity. Emerging visual computing workloads …
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Learning-Based Modeling of Weather and Climate Events Related To El Niño Phenomenon via Differentiable Programming and Empirical Decompositions
… methods lack. The main methods explored are 1) differentiable Programming, as a means of construction of novel self-learning models through which the meaningfulness of parameters arises from emergent phenomenon and 2) empirical decompositions, which are driven by an adaptive rather than rigid …
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Higher-Order Automatic Differentiation and Its Applications
Differentiable programming is a new paradigm for modeling and optimization in many fields of science and engineering, and automatic differentiation (AD) algorithms are at the heart of differentiable programming. Existing methods to achieve higher-order AD often suffer from one or more of the …
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Language Evolution for Parallel and Scientific Computing
… a decades’ long dream of both scientists and programming language designers to make the development for and usage of high-performance computing easier. Many attempts have failed, perhaps because this is a hard problem, perhaps because the social motivation and the required steps to achieve …
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Computational illumination for portrait photography and inverse graphics
… a novel formulation for fast and accurate differentiable rendering based on analytical anti-aliasing. It is demonstrated how this renderer can be used for inverse graphics problems. The thesis concludes with a discussion on how differentiable programming can be combinded with data-driven …
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Advanced Reconstruction Techniques for CUORE: Searching Beyond the Standard Model with Cryogenic Calorimeters
… − 1/5 that of an electron. Lastly, we introduce differentiable programming methods for the end-to-end training of neural ordinary differential equations to model thermal pulse dynamics within CUORE calorimeter channels. These methods and results improve understanding of detector response, enable …
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From efficient high-order methods to scientific machine learning.
… algorithms. Lastly, we introduce Torchfire, a differentiable programming interface that combines PyTorch and Firedrake to perform model-constrained deep learning of solutions of parameterized PDEs and PDE-constrained inverse problems. It leverages PyTorch's high-level interface for training …
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From efficient high-order methods to scientific machine learning.
… algorithms. Lastly, we introduce Torchfire, a differentiable programming interface that combines PyTorch and Firedrake to perform model-constrained deep learning of solutions of parameterized PDEs and PDE-constrained inverse problems. It leverages PyTorch's high-level interface for training …
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Static analysis of differentiable programs
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms