This work shows how to efficiently perform arbitrary contractions of the derivative tensor of arbitrary order for multivariate functions by properly constructing the input tangents to univariate high-order AD, which can be used to randomize any differential operator efficiently.
Tensor networks are powerful formats for compressing large-scale data. However, their application to general data processing has been limited by the difficulty of performing nonlinear operations. Here, we introduce iterative tensor network transformations (ITNTs), a general algorithmic framework for the element-wise ev...
Xiao Wang, Tomohiro Hashizume, Pia Siegl et al.· 2 citations
BaKron is an efficient solver that combines anti-diagonal parallelism with a recursive divide-and-conquer construction that matches the cubic scaling of GPTQ while exploiting richer curvature information.
KIOPS-TT and RK2EXPINT-TT are developed as extensions of the original KIOPS and RK2EXPINT algorithms, reformulating the scheme of explicit exponential Runge-Kutta integrators for tensors and augmenting the stiffness tensor to compute linear combinations of $\varphi$-functions acting on tensors through a single evaluati...
R. Weigel, Tom-Christian Riemer, Martin Stoll· arXiv.org· 0 citations
This work presents a method to accelerate the optimization of learning high dimensional functions using deep neural network (DNN), and studies the effect of adding features which distill pretrained DNN into TNs using a discretize and decompose strategy.
FNO-Speed, an integrated solution incorporating the multi-level parallel FNO-aware mapping and tiling GEMM optimization strategy and the custom-sized high-frequency signal filtering scheme, is proposed, fully demonstrating the effectiveness of the FNO-Speed optimization strategy in improving FNO performance.
Tao Song, Ying Ouyang, Xiangyu Meng et al.· ACM Transactions on Architec...· 0 citations
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